{"id":82484,"date":"2025-07-01T15:38:40","date_gmt":"2025-07-01T10:08:40","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=82484"},"modified":"2026-02-11T17:48:43","modified_gmt":"2026-02-11T12:18:43","slug":"what-is-object-detection","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/what-is-object-detection\/","title":{"rendered":"What is Object Detection? A Beginner\u2019s Guide [2026]"},"content":{"rendered":"\n<p>Have you ever wondered how a ball is tracked in a live cricket match? How is a self-driving car able to identify objects while on the road? All this happens through an amazing technique called \u2018OBJECT DETECTION\u2019.&nbsp;<\/p>\n\n\n\n<p>But it can be a little confusing for beginners to understand all its hows and whys. Hence, in this blog, I\u2019m going to give you an idea of how this works in the real world. I\u2019m going to start with what object detection is and give you an explanation with very simple examples. We are also going to learn about some of the Deep learning techniques used for Object detection. Let\u2019s get started\u2026<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What is Object Detection?<\/strong><\/h2>\n\n\n\n<p>As the name suggests, <strong>Object Detection<\/strong> is all about detecting objects in a visual scene. These could be people, animals, cars, furniture, accessories, traffic lights, buildings\u2014you name it. Object Detection is a <a href=\"https:\/\/www.guvi.in\/blog\/computer-vision-projects-for-beginners\/\" target=\"_blank\" rel=\"noreferrer noopener\">computer vision<\/a> task that detects objects in images and videos.<\/p>\n\n\n\n<p>The motive of Object detection is to recognize, identify, and localize all the known objects in the still image or video data. This information from the object detector is used for wide applications in the real world, and Data scientists play an important role in building algorithms using Deep Learning techniques.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>What\u2019s happening under the hood?<\/strong><\/h3>\n\n\n\n<p>You&#8217;re essentially telling the computer:<\/p>\n\n\n\n<ul>\n<li><strong>What<\/strong> is present in an image (classification), and<\/li>\n\n\n\n<li><strong>Where<\/strong> it&#8217;s located (localization)<\/li>\n<\/ul>\n\n\n\n<p>This combination makes object detection a powerful subdomain of Computer Vision. It&#8217;s used in everything from medical imaging to facial recognition, self-driving cars to factory automation.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter is-resized\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcj9SsMAJP356gIIAzWR101n-SMcq33hDSGviVeAw2sm-hMGzWTGPVekVhnPClCQT78HHTwD1KEumXRRWqd1b1rx09mXLtpfFf2SR8u1k23qMCYOyNabS-K290JxTLRxwzVIvZ20A?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"object detection\" style=\"aspect-ratio:1.4957507082152974;width:628px;height:auto\" title=\"\"><\/figure><\/div>\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Object Detection Matters<\/strong><\/h2>\n\n\n\n<p>You might ask, &#8220;Why should I even care about object detection?&#8221;<\/p>\n\n\n\n<p>Well, here\u2019s how it directly impacts your life:<\/p>\n\n\n\n<ul>\n<li>Your face unlock feature? Object detection.<\/li>\n\n\n\n<li>Virtual try-on filters on Instagram? Object detection.<\/li>\n\n\n\n<li>Ball tracking in sports? You guessed it\u2014object detection.<\/li>\n\n\n\n<li>Security surveillance? Yep, again.<\/li>\n<\/ul>\n\n\n\n<p>The ultimate goal is to recognize, classify, and localize objects in real-time from static images or continuous video feeds.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Must-Know Techniques used for Object Detection<\/strong><\/h2>\n\n\n\n<p>Object Detection can be done using two approaches: <a href=\"https:\/\/www.guvi.in\/blog\/machine-learning-vs-deep-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Machine Learning and Deep Learning<\/a>.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXfHq56XS3SpsS54zgqYxg4pvgMV1VyfpUHC863olMUGrylihIPzevTzmPKc9DRnJtBJMsmHR1r2EmnHPXbCc8po4oVqFOwgIxia2Ryigfw_LdebDtQrzQ35TJ8q2zy2_5ga5NDx?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"Must-Know Techniques used for Object Detection\" title=\"\"><\/figure><\/div>\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Machine Learning (ML)-Based<\/strong><\/h3>\n\n\n\n<p>In <a href=\"https:\/\/www.guvi.in\/blog\/introduction-to-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Machine Learning<\/a>, the data is entered manually for classification. It is taken as supervised machine learning, so the pre-trained models are used to trigger the object detectors. In ML, you extract features like color, edges, shapes manually, and then use models like:<\/p>\n\n\n\n<ul>\n<li><a href=\"https:\/\/www.guvi.in\/blog\/what-is-svm-in-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Support Vector Machines (SVM)<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.guvi.in\/blog\/decision-tree-in-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Decision Trees<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.guvi.in\/blog\/knn-algorithm-in-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">k-Nearest Neighbors (kNN)<\/a><\/li>\n<\/ul>\n\n\n\n<p>You rely heavily on feature engineering, which means you&#8217;re handpicking what data should matter. Pretrained models are often used here, and it\u2019s usually supervised learning.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Deep Learning (DL)-Based<\/strong><\/h3>\n\n\n\n<p>This is the modern standard. In <a href=\"https:\/\/www.guvi.in\/blog\/deep-learning-and-neural-network\/\" target=\"_blank\" rel=\"noreferrer noopener\">deep learning<\/a>, automatic feature selection is done using convolutional neural network methods. Deep learning models like Convolutional <a href=\"https:\/\/www.guvi.in\/blog\/must-know-neural-networks-for-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">Neural Networks<\/a> (CNNs) automatically learn useful features without manual effort. It\u2019s faster, more scalable, and more accurate.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Summary:<\/strong><\/h4>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Feature<\/strong><\/td><td><strong>Machine Learning<\/strong><\/td><td><strong>Deep Learning<\/strong><\/td><\/tr><tr><td>Feature Selection<\/td><td>Manual<\/td><td>Automatic (CNNs)<\/td><\/tr><tr><td>Accuracy<\/td><td>Moderate<\/td><td>High<\/td><\/tr><tr><td>Scalability<\/td><td>Limited<\/td><td>High<\/td><\/tr><tr><td>Popular Models<\/td><td>SVM, HOG<\/td><td>YOLO, SSD, R-CNN<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong><em>NOTE: In this blog, you&#8217;re focusing on deep learning techniques, since they power most real-world object detection systems today.<\/em><\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Deep Learning Techniques<\/strong><\/h2>\n\n\n\n<p>Object detection is used to understand what\u2019s in the image and where the objects are found in the image. To achieve this task, there are two different approaches.<\/p>\n\n\n\n<ol>\n<li>Making a fixed number of predictions ( one stage )<\/li>\n\n\n\n<li>A network is proposed to find objects and use another network to fine-tune the results to predict the final output (two-stage)<\/li>\n<\/ol>\n\n\n\n<figure class=\"wp-block-image is-resized\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcAkiYBgPBrNk7qW1NteCoIzoCFmqXc-jYOisW71foHLHtANdX15Lo8CE1b05aAbD-L0BkTQYD2kH-xqJwtN8z4Xg9GT_3KnlmDBRpVfdTAm7rHIaSZwbmtSEGD9RflQmGXiDJf?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"Deep Learning Techniques\" style=\"aspect-ratio:1.5535714285714286;width:840px;height:auto\" title=\"\"><\/figure>\n\n\n\n<p><br>There are many deep learning techniques used for object detection. The image below shows the popular techniques used for object Detection.<\/p>\n\n\n\n<figure class=\"wp-block-image is-resized\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdYBLEadaVDNpjc27C5E_JFHuDy0xnS7noeS83F3YBV_jc6ahqx-HoO-jv-QjA5dXcO76VHuvelB-DDalcBg5pNYFB2HFugRvj9IOMih0ce6DQ2grvnQJQyDoZBuG8BTmhazxf-gw?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"Deep Learning Techniques\" style=\"aspect-ratio:1.300771208226221;width:839px;height:auto\" title=\"\"><\/figure>\n\n\n\n<p>In this blog, I\u2019m going to explain one of the fastest, popular, efficient, and widely used techniques, YOLO.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>YOLO &#8211; A State-of-the-Art Object Detection Algorithm<\/strong><\/h2>\n\n\n\n<p>YOLO is an abbreviation for \u201cYou Only Live Once\u201d. This was invented in 2015, and it outperforms all the previously used techniques. YOLO is the state-of-the-art object detection algorithm, and it has become the standard way of detecting objects in the field of computer vision because of its quick performance and accuracy.&nbsp;<\/p>\n\n\n\n<p>It is considered a Regression problem, and it provides the probability of the class which belongs to the detected images. The YOLO algorithm divides the image into N grids or boxes, where each grid has an equal dimension region of S x S. Now, each grid or box is responsible for identifying the image it contains.<\/p>\n\n\n\n<p><strong><em>For example,<\/em><\/strong> consider the image classification problem with this image. Let\u2019s say that we are trying to identify whether the image has a dog or a <strong><em>person<\/em><\/strong><\/p>\n\n\n\n<p>Here I\u2019m taking only 2 classes for easier understanding of the concept. So we have only two classes. C1 as Dog and C2 as Person.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 1)<\/strong><\/h3>\n\n\n\n<p><img decoding=\"async\" width=\"624\" height=\"351\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcFOTL_n2VkMdeZN2-azLKF4zdAj-F6BSoBfypDkBrUx-KmB_VWLofl8YHM_jFM-SQ3rM_Um_pNQGMSp16WqGkCwMh5Gp8JSlo6k-lhrhvFKbWjBzX6kPhpm7USAEOR4mdnOERz?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"\" title=\"\"><\/p>\n\n\n\n<p>In this image, the output is clear and simple; it gives the Dog as 1 and the Person as 0. The bounding box locates exactly where the identified dog is, or the position of the dog in the image.&nbsp;<\/p>\n\n\n\n<p>To produce this output, CNN is creating a vector with the seven values. You will better understand by the image given below:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcSFurGSWU1m-RxXYcEpjFKmgsyk7b4pcyFD-lhKNLG7gVDOrw9innx_2QTpQ824r1H7B3XK_aAe2D5ixmC1X3b8O8pX1lRlKGLRbZwwywKd-UZPDZnHkms2m3k82lKHevhIdyq6g?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"Deep Learning Techniques\" title=\"\"><\/figure>\n\n\n\n<p>Now, I have added the image of a girl to find the vector. We can see the Dog class is now 0, and the Person class is 1. The probability of the object, Pc, is 1 as it can detect a person in the image.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXf-_iv8IkoUcUhCICNj1AlYeb0B9DcV7BO8uVPvcXVMBYBIMDfWpqA7r751VUjepYGuCVH0Mi4oOb5J8dFRwimTL_tnFSL-QQq1CXTvhSRciMzKHyZYa4690msVS8Ei2Va2ruQ-?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"Deep Learning Techniques\" title=\"\"><\/figure>\n\n\n\n<p>What if there is no object in the image?!&nbsp; What if there is no person or dog in the image? The probability class will become zero, as it is in the image below.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcUG_IntucshvCVmEEWTi1EweFdArWfodlZIVkSZvGRTrecucOgtLmJ1uzu1tT-giB6PnMYuWtlUkmQnc_f9JwdtA4UgQAAK5YSJvuXTD8XNBj77DYwSmWgI3zB8af-DAoMUhnesg?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"Deep Learning Techniques\" title=\"\"><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 2)<\/strong><\/h3>\n\n\n\n<p>After this object localization, the input image is divided into grids of equal dimensions and the final detection is done based on the confidence score of bounding boxes and the class probability of the objects.&nbsp;<\/p>\n\n\n\n<p>Let me explain with an example. Consider this image given below where there are two objects, the dog and a person.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcXPdEbxKW_aRnRzYsVBVgbkCSJpLfYU9ND2kihEIc0sRNG4cTgR4dhgJzt-GDuwIp21lul63tr_OaX1QP5j7Td4S0unJEkTTDA9b8pCs-IFFlODyp4bQnQvpxLJPhbVY05vXxJ?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"Deep Learning Techniques\" title=\"\"><\/figure>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXeDOVgJcwQDXtZttGGGf08rc_xqCChghyOc7tQ2BHC3Qb20l1cG3Ndr-JQw4oGIatMBD8RQNSR_wn3_pd1bFIwrG2o6WbiG-ETeujiLUkMn3r-w-mAPlkGqJFCoq81a2m7IdkFnxw?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"Deep Learning Techniques\" title=\"\"><\/figure>\n\n\n\n<p>This is how the <a href=\"https:\/\/en.wikipedia.org\/wiki\/You_Only_Look_Once\" target=\"_blank\" rel=\"noreferrer noopener\">YOLO <\/a>technique works to detect objects in the images. I hope you now have an idea about the YOLO architecture and how it detects objects. Do reach out to me in the comments section in case of any doubts.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>YOLO Architecture and step-by-step working<\/strong><\/h3>\n\n\n\n<p>Here is the architecture of how YOLO works.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXc2iDNoCE3wHcP19xGT1zADBtOMu7HKokJeUz6vcobXFTLtodiR25AV36mAUTMEnJNgw5cvuwrHJ2ibcWO6xBmSaWSOyvYhPiXX0-c3xTYsdAo5-pPeoQFkhCR1EQ_c81uX2aqu?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"Deep Learning Techniques\" title=\"\"><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>YOLO in Action \u2013 Step by Step<\/strong><\/h3>\n\n\n\n<p>Let\u2019s revise everything we learnt about the working of YOLO in short:<\/p>\n\n\n\n<ol>\n<li><strong>Input Image<\/strong><strong><br><\/strong> \u2192 Divided into a grid of cells<\/li>\n\n\n\n<li><strong>Each Cell Predicts<\/strong><strong><br><\/strong> \u2192 Bounding boxes + class probabilities<\/li>\n\n\n\n<li><strong>Confidence Scores Calculated<\/strong><strong><br><\/strong> \u2192 Based on IOU (Intersection Over Union) between predicted and actual boxes<\/li>\n\n\n\n<li><strong>Non-Maximum Suppression<\/strong><strong><br><\/strong> \u2192 Keeps the best boxes, removes duplicates<\/li>\n\n\n\n<li><strong>Final Output<\/strong><strong><br><\/strong> \u2192 Accurate object detection with speed<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Real-Life Object Detection Applications<\/strong><\/h2>\n\n\n\n<p>Object detection is everywhere\u2014often in ways you don\u2019t even realize. Here are some exciting real-world use cases you interact with:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1) Self-Driving Cars<\/strong><\/h3>\n\n\n\n<p>Cars detect road signs, traffic lights, other vehicles, pedestrians, and animals\u2014every frame is analyzed to make split-second decisions.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXfe3-2-dBWDxazH3nwh85wh17X9OLlfVX_kcBRMJ68aajflNLfACYI24W6CAn7yKVnKnJ_0zTaBLLU-2XdNDFYvTecfdDLzwAT3D8OodDWeJLFWXw3ErI25ktWXgDc0_60Zg8dbkw?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"Self-Driving Cars\" title=\"\"><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2) Sports Analytics<\/strong><\/h3>\n\n\n\n<p>Ever seen how the ball is tracked in cricket or football replays? That\u2019s object detection. It even helps count steps or track gym workouts!<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXc0HQbJzEMNTQDgDvT-ul9P5kwn2D1u-wHrp_uRjaHjx2xqnJrVuUS1MZofOC65Xovw7gwvzuz_pRPW0acikQUSEpT6gnwjbEJGb3mM6RsYe4ujFT5kyyvnf_-wuAmtr_c3ark03Q?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\" Sports Analytics\" title=\"\"><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3) Medical Imaging<\/strong><\/h3>\n\n\n\n<p>From identifying tumors in X-rays to tracking organs in ultrasound videos, object detection is revolutionizing diagnosis.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXd6RiHQ-qWYBR2ZyRQGlLsZRlwbWTIhSv_5L--NF-uy2YJWsFePtHQA2i6nJTk9enVs3FMC1kO8A6r4fQ7w3QBZAwre2RrULsYxAMzbDtkXb4z5kLPirixMb3tjaC3DuGIEIcnHNA?key=TZ7MaXJ36PQP_NwCzBALqQ\" alt=\"Medical Imaging\" title=\"\"><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Tools You Can Try as a Beginner<\/strong><\/h2>\n\n\n\n<p>If you\u2019re curious to try it hands-on, here are the tools to help you get started:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Tool<\/strong><\/td><td><strong>Purpose<\/strong><\/td><\/tr><tr><td><strong>YOLOv8 (Ultralytics)<\/strong><\/td><td>Latest, easiest YOLO version<\/td><\/tr><tr><td><strong>Google Colab<\/strong><\/td><td>Run YOLO code for free using GPUs<\/td><\/tr><tr><td><strong>LabelImg<\/strong><\/td><td>Tool to annotate images (draw bounding boxes)<\/td><\/tr><tr><td><strong>Roboflow<\/strong><\/td><td>Upload, preprocess, and train datasets<\/td><\/tr><tr><td><strong>OpenCV + <\/strong><a href=\"https:\/\/www.guvi.in\/hub\/python\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Python<\/strong><\/a><\/td><td>Great for building real-time detection systems<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong><em>Ready to go from theory to real-world AI skills? Check out HCL GUVI\u2019s <\/em><\/strong><a href=\"https:\/\/www.guvi.in\/mlp\/artificial-intelligence-and-machine-learning-course?utm_source=blog&amp;utm_medium=hyperlink&amp;utm_campaign=What+is+Object+Detection%3F+A+Beginner%E2%80%99s+Guide+%5B2025%5D\" target=\"_blank\" rel=\"noreferrer noopener\"><strong><em>Artificial Intelligence &amp; Machine Learning Course<\/em><\/strong><\/a><strong><em>, designed by IIT-Madras &amp; industry experts, it teaches you hands-on object detection, deep learning, and more, using real-time projects and tools like YOLO, OpenCV, and TensorFlow.<\/em><\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Concluding Thoughts\u2026<\/strong><\/h2>\n\n\n\n<p>Object Detection is in a wide range of industries, where its uses range from personal security to productivity in the workplace. You\u2019ve just taken a deep dive into the fascinating world of Object Detection. From recognizing people in photos to detecting diseases in medical scans, this technology is shaping our future, and now you understand how.<\/p>\n\n\n\n<p>There are endless possibilities when it comes to future use cases. I really hope you have learnt some information from this blog. Happy Learning \ud83d\ude42<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Have you ever wondered how a ball is tracked in a live cricket match? How is a self-driving car able to identify objects while on the road? All this happens through an amazing technique called \u2018OBJECT DETECTION\u2019.&nbsp; But it can be a little confusing for beginners to understand all its hows and whys. Hence, in [&hellip;]<\/p>\n","protected":false},"author":16,"featured_media":84069,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[933,316],"tags":[],"views":"2137","authorinfo":{"name":"Jaishree Tomar","url":"https:\/\/www.guvi.in\/blog\/author\/jaishree\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-is-Object-Detection_-A-Beginners-Guide-300x116.png","jetpack_featured_media_url":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2025\/07\/What-is-Object-Detection_-A-Beginners-Guide.png","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/82484"}],"collection":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/users\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=82484"}],"version-history":[{"count":7,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/82484\/revisions"}],"predecessor-version":[{"id":100921,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/82484\/revisions\/100921"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/84069"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=82484"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=82484"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=82484"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}