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<title>Object detection</title>

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<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">Object detection</span></span>

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<p><b>Object detection</b> is a computer technology related to <a href="Computer_vision" title="Computer vision">computer vision</a> and <a href="Image_processing" class="mw-redirect" title="Image processing">image processing</a> that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.<sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> Well-researched domains of object detection include <a href="Face_detection" title="Face detection">face detection</a> and <a href="Pedestrian_detection" title="Pedestrian detection">pedestrian detection</a>. Object detection has applications in many areas of computer vision, including <a href="Image_retrieval" title="Image retrieval">image retrieval</a> and <a href="Video_surveillance" class="mw-redirect" title="Video surveillance">video surveillance</a>.

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<p>It is widely used in <a href="Computer_vision" title="Computer vision">computer vision</a> tasks such as <a href="Automatic_image_annotation" title="Automatic image annotation">image annotation</a>,<sup id="cite_ref-GuanHe2012_2-0" class="reference"><a href="#cite_note-GuanHe2012-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> vehicle counting,<sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> <a href="Activity_recognition" title="Activity recognition">activity recognition</a>,<sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> <a href="Face_detection" title="Face detection">face detection</a>, <a href="Face_recognition" class="mw-redirect" title="Face recognition">face recognition</a>, <a href="Object_co-segmentation" title="Object co-segmentation">video object co-segmentation</a>. It is also used in <a href="Video_tracking" title="Video tracking">tracking objects</a>, for example tracking a ball during a football match, tracking movement of a cricket bat, or tracking a person in a video.

</p><p>Often, the test images are sampled from a different data distribution, making the object detection task significantly more difficult.<sup id="cite_ref-:0_5-0" class="reference"><a href="#cite_note-:0-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> To address the challenges caused by the domain gap between training and test data, many unsupervised domain adaptation approaches have been proposed.<sup id="cite_ref-:0_5-1" class="reference"><a href="#cite_note-:0-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-6" class="reference"><a href="#cite_note-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-7" class="reference"><a href="#cite_note-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-8" class="reference"><a href="#cite_note-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-9" class="reference"><a href="#cite_note-9"><span class="cite-bracket">[</span>9<span class="cite-bracket">]</span></a></sup> A simple and straightforward solution for reducing the domain gap is to apply an image-to-image translation approach, such as cycle-GAN.<sup id="cite_ref-10" class="reference"><a href="#cite_note-10"><span class="cite-bracket">[</span>10<span class="cite-bracket">]</span></a></sup> Among other uses, cross-domain object detection is applied in autonomous driving, where models can be trained on a vast amount of video game scenes, since the labels can be generated without manual labor.

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<div class="mw-heading mw-heading2"><h2 id="Concept">Concept</h2></div>

<p>Every object class has its own special <a href="Feature_(computer_vision)" title="Feature (computer vision)">features</a> that help in classifying the class – for example all <a href="Circle" title="Circle">circles</a> are round.

Object class detection uses these special features. For example, when looking for circles, objects that are at a particular distance from a point (i.e. the center) are sought. Similarly, when looking for squares, objects that are <a href="Perpendicular" title="Perpendicular">perpendicular</a> at corners and have equal side lengths are needed. A similar approach is used for <a href="Facial_recognition_system" title="Facial recognition system">face identification</a> where eyes, nose, and lips can be found and <a href="Feature_(computer_vision)" title="Feature (computer vision)">features</a> like skin color and distance between eyes can be found.

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<div class="mw-heading mw-heading2"><h2 id="Benchmarks">Benchmarks</h2></div>

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</style><div class="thumb tmulti tright"><div class="thumbinner multiimageinner" style="width:304px;max-width:304px"><div class="trow"><div class="tsingle" style="width:302px;max-width:302px"><div class="thumbimage"><span typeof="mw:File"></span></div></div></div><div class="trow"><div class="tsingle" style="width:302px;max-width:302px"><div class="thumbimage"><span typeof="mw:File"></span></div></div></div><div class="trow"><div class="tsingle" style="width:302px;max-width:302px"><div class="thumbimage"><span typeof="mw:File"></span></div></div></div><div class="trow" style="display:flex"><div class="thumbcaption">Intersection over union as a similarity measure for object detection on images – an important task in <a href="Computer_vision" title="Computer vision">computer vision</a>.</div></div></div></div>

<p>For object localization, true positive is often measured by the thresholded <a href="Intersection_over_union" class="mw-redirect" title="Intersection over union">intersection over union</a>. For example, if there is a traffic sign in the image, with a bounding box drawn by a human ("ground truth label"), then a neural network has detected the traffic sign (a <b>true positive</b>) at 0.5 threshold iff it has drawn a bounding box whose IoU with the ground truth is above 0.5. Otherwise, the bounding box is a <b>false positive</b>.

</p><p>If there is only a single ground truth bounding box, but multiple predictions, then the IoU of each prediction is calculated. The prediction with the highest IoU is a true positive if it is above threshold, else it is a false positive. All other predicted bounding boxes are false positives. If there is no prediction with an IoU above the threshold, then the ground truth label has a <b>false negative</b>.

</p><p>For simultaneous object localization and classification, a true positive is one where the class label is correct, and the bounding box has an IoU exceeding the threshold.

</p><p>Simultaneous object localization and classification is benchmarked by the <b>mean average precision</b> (mAP). The average precision (AP) of the network for a class of objects is the area under the <a href="Receiver_operating_characteristic" title="Receiver operating characteristic">precision-recall curve</a> as the IoU threshold is varied. The mAP is the average of AP over all classes.

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<div class="mw-heading mw-heading2"><h2 id="Methods">Methods</h2></div>

<div class="thumb tmulti tright"><div class="thumbinner multiimageinner" style="width:392px;max-width:392px"><div class="trow"><div class="tsingle" style="width:167px;max-width:167px"><div class="thumbimage" style="height:188px;overflow:hidden"><span typeof="mw:File"></span></div><div class="thumbcaption">Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict <a href="Starfish" title="Starfish">starfish</a> and <a href="Sea_urchin" title="Sea urchin">sea urchins</a>, which are correlated with "nodes" that represent visual <a href="Feature_(computer_vision)" title="Feature (computer vision)">features</a>. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and oval shape. However, the instance of a ring textured sea urchin creates a weakly weighted association between them.</div></div><div class="tsingle" style="width:221px;max-width:221px"><div class="thumbimage" style="height:188px;overflow:hidden"><span typeof="mw:File"></span></div><div class="thumbcaption">Subsequent run of the network on an input image (left):<sup id="cite_ref-11" class="reference"><a href="#cite_note-11"><span class="cite-bracket">[</span>11<span class="cite-bracket">]</span></a></sup> The network correctly detects the starfish. However, the weakly weighted association between ringed texture and sea urchin also confers a weak signal to the latter from one of two intermediate nodes. In addition, a shell that was not included in the training gives a weak signal for the oval shape, also resulting in a weak signal for the sea urchin output. These weak signals may result in a <a href="False_positive" class="mw-redirect" title="False positive">false positive</a> result for sea urchin.<br>In reality, textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.</div></div></div></div></div>

<p>Methods for object detection generally fall into either neural network-based or non-neural approaches. For non-neural approaches, it becomes necessary to first define features using one of the methods below, then using a technique such as <a href="Support_vector_machine" title="Support vector machine">support vector machine</a> (SVM) to do the classification. On the other hand, neural techniques are able to do end-to-end object detection without specifically defining features, and are typically based on <a href="Convolutional_neural_network" title="Convolutional neural network">convolutional neural networks</a> (CNN).

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<ul><li>Non-neural approaches:

<ul><li><a href="Viola%E2%80%93Jones_object_detection_framework" title="Viola–Jones object detection framework">Viola–Jones object detection framework</a> based on <a href="Haar-like_feature" title="Haar-like feature">Haar features</a></li>

<li><a href="Scale-invariant_feature_transform" title="Scale-invariant feature transform">Scale-invariant feature transform (SIFT)</a></li>

<li><a href="Histogram_of_oriented_gradients" title="Histogram of oriented gradients">Histogram of oriented gradients (HOG)</a> features<sup id="cite_ref-12" class="reference"><a href="#cite_note-12"><span class="cite-bracket">[</span>12<span class="cite-bracket">]</span></a></sup></li></ul></li>

<li>Neural network approaches:

<ul><li>OverFeat.<sup id="cite_ref-13" class="reference"><a href="#cite_note-13"><span class="cite-bracket">[</span>13<span class="cite-bracket">]</span></a></sup></li>

<li><a href="Region_Based_Convolutional_Neural_Networks" title="Region Based Convolutional Neural Networks">Region Proposals</a> (R-CNN,<sup id="cite_ref-14" class="reference"><a href="#cite_note-14"><span class="cite-bracket">[</span>14<span class="cite-bracket">]</span></a></sup> Fast R-CNN,<sup id="cite_ref-15" class="reference"><a href="#cite_note-15"><span class="cite-bracket">[</span>15<span class="cite-bracket">]</span></a></sup> Faster R-CNN,<sup id="cite_ref-16" class="reference"><a href="#cite_note-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup> cascade R-CNN.<sup id="cite_ref-Pang_Chen_Shi_Feng_2019_17-0" class="reference"><a href="#cite_note-Pang_Chen_Shi_Feng_2019-17"><span class="cite-bracket">[</span>17<span class="cite-bracket">]</span></a></sup>)</li>

<li>Single Shot MultiBox Detector (SSD) <sup id="cite_ref-19" class="reference"><a href="#cite_note-19"><span class="cite-bracket">[</span>19<span class="cite-bracket">]</span></a></sup></li>

<li>Single-Shot Refinement Neural Network for Object Detection (RefineDet) <sup id="cite_ref-20" class="reference"><a href="#cite_note-20"><span class="cite-bracket">[</span>20<span class="cite-bracket">]</span></a></sup></li>

<li>Retina-Net <sup id="cite_ref-21" class="reference"><a href="#cite_note-21"><span class="cite-bracket">[</span>21<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-Pang_Chen_Shi_Feng_2019_17-1" class="reference"><a href="#cite_note-Pang_Chen_Shi_Feng_2019-17"><span class="cite-bracket">[</span>17<span class="cite-bracket">]</span></a></sup></li>

<li>Deformable convolutional networks <sup id="cite_ref-22" class="reference"><a href="#cite_note-22"><span class="cite-bracket">[</span>22<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-23" class="reference"><a href="#cite_note-23"><span class="cite-bracket">[</span>23<span class="cite-bracket">]</span></a></sup></li></ul></li></ul>

<ul><li><a href="Feature_detection_(computer_vision)" class="mw-redirect" title="Feature detection (computer vision)">Feature detection (computer vision)</a></li>

<li><a href="Moving_object_detection" title="Moving object detection">Moving object detection</a></li>

<li><a href="Small_object_detection" title="Small object detection">Small object detection</a></li>

<li><a href="Outline_of_object_recognition" title="Outline of object recognition">Outline of object recognition</a></li>

<li><a href="Teknomo%E2%80%93Fernandez_algorithm" title="Teknomo–Fernandez algorithm">Teknomo–Fernandez algorithm</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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<ul><li><cite id="CITEREFZouChenShiGuo2023" class="citation journal cs1">Zou, Zhengxia; Chen, Keyan; Shi, Zhenwei; Guo, Yuhong; Ye, Jieping (March 2023). <span class="id-lock-subscription" title="Paid subscription required"><a rel="nofollow" class="external text" href="https://ieeexplore.ieee.org/document/10028728/">"Object Detection in 20 Years: A Survey"</a></span>. <i>Proceedings of the IEEE</i>. <b>111</b> (3): <span class="nowrap">257–</span>276. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FJPROC.2023.3238524">10.1109/JPROC.2023.3238524</a>. <a href="ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/0018-9219">0018-9219</a>.</cite></li></ul>

<div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2></div>

<ul><li><cite id="CITEREFWeng2017" class="citation web cs1">Weng, Lilian (2017-10-29). <a rel="nofollow" class="external text" href="https://lilianweng.github.io/posts/2017-10-29-object-recognition-part-1/">"Object Detection for Dummies Part 1: Gradient Vector, HOG, and SS"</a>. <i>lilianweng.github.io</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2024-09-11</span></span>.</cite></li>

<li><cite id="CITEREFWeng2017" class="citation web cs1">Weng, Lilian (2017-12-15). <a rel="nofollow" class="external text" href="https://lilianweng.github.io/posts/2017-12-15-object-recognition-part-2/">"Object Detection for Dummies Part 2: CNN, DPM and Overfeat"</a>. <i>lilianweng.github.io</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2024-09-11</span></span>.</cite></li>

<li><cite id="CITEREFWeng2017" class="citation web cs1">Weng, Lilian (2017-12-31). <a rel="nofollow" class="external text" href="https://lilianweng.github.io/posts/2017-12-31-object-recognition-part-3/">"Object Detection for Dummies Part 3: R-CNN Family"</a>. <i>lilianweng.github.io</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2024-09-11</span></span>.</cite></li>

<li><cite id="CITEREFWeng2018" class="citation web cs1">Weng, Lilian (2018-12-27). <a rel="nofollow" class="external text" href="https://lilianweng.github.io/posts/2018-12-27-object-recognition-part-4/">"Object Detection Part 4: Fast Detection Models"</a>. <i>lilianweng.github.io</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2024-09-11</span></span>.</cite></li>

<li><a rel="nofollow" class="external text" href="https://web.archive.org/web/20150418092531/http://www.mmp.rwth-aachen.de/publications/pdf/mikolajczyk-multiclass-cvpr06.pdf">Multiple object class detection</a></li>

<li><a rel="nofollow" class="external text" href="https://qilin-zhang.github.io/_pages/pdfs/Segment-Tube_Spatio-Temporal_Action_Localization_in_Untrimmed_Videos_with_Per-Frame_Segmentation.pdf">Spatio-temporal action localization</a></li>

<li><a rel="nofollow" class="external text" href="https://astica.ai/vision/object-detection/">Online Object Detection Demo</a></li>

<li><a rel="nofollow" class="external text" href="https://qilin-zhang.github.io/_pages/pdfs/Joint_Video_Object_Discovery_and_Segmentation_by_Coupled_Dynamic_Markov_Networks.pdf">Video object detection and co-segmentation</a></li></ul></div><div><div class="zim-footer">

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