single shot object detection

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22 January 2021

/R35 7.9701 Tf Q /Subject (2018 IEEE Conference on Computer Vision and Pattern Recognition) q /R33 9.9626 Tf /Group << >> 59.441 4.33906 Td 0 g endobj Single Shot Detector. /ExtGState << [ (imental) -331.013 (r) 37.0196 (esults) -331.996 (on) -330.994 (both) -330.982 (P) 89.9887 (ASCAL) -331.983 (V) 29.9987 (OC) -330.988 (and) -330.989 (MS) -330.996 (COCO) -331.991 (de\055) ] TJ (17) Tj /R73 95 0 R /R130 154 0 R The SSD object detection network can be thought of as having two sub-networks. 10 0 0 10 0 0 cm T* /R33 54 0 R /R33 9.9626 Tf 0 1 0 rg This section describes the signature for Single-Shot Detector models converted to TensorFlow Lite from the TensorFlow Object Detection API. q If we remember YOLO, there are 7×7 locations at the end with 2 bounding boxes for each location. In classification, it is assumed that object occupies a significant portion of the image like the object in figure 1. stream /s7 gs Use Icecream Instead, 7 A/B Testing Questions and Answers in Data Science Interviews, 10 Surprisingly Useful Base Python Functions, How to Become a Data Analyst and a Data Scientist, The Best Data Science Project to Have in Your Portfolio, Three Concepts to Become a Better Python Programmer, Social Network Analysis: From Graph Theory to Applications with Python, After going through a certain of convolutions for feature extraction, we obtain, Conv7: 19×19×6 = 2166 boxes (6 boxes for each location), Conv8_2: 10×10×6 = 600 boxes (6 boxes for each location), Conv9_2: 5×5×6 = 150 boxes (6 boxes for each location), Conv10_2: 3×3×4 = 36 boxes (4 boxes for each location), Conv11_2: 1×1×4 = 4 boxes (4 boxes for each location). /Resources 22 0 R /R126 157 0 R /R30 32 0 R /R43 9.9626 Tf /R33 54 0 R ET [ (such) -243.987 (as) -243.997 (Y) 29.9981 (OLO) -243.989 (\133) ] TJ 0.1 0 0 0.1 0 0 cm >> 62.2918 0 Td Single-shot methods like SSD suffer from extremely by class imbalance. /F1 282 0 R /R90 129 0 R /R33 11.9552 Tf 0 g >> Q >> q /R57 114 0 R Data Augmentation is crucial, which improves from 65.5% to 74.3% mAP. [ (Pre) 24.983 (vious) -321.997 (single) -321.999 (shot) -322.982 (object) -322.009 (detectors\054) -340.005 (such) -322.012 (as) -321.983 (SSD\054) -323.01 (use) ] TJ For the picture below, there are 9 Santas in the lower left corner but one of the single shot … 1 0 0 1 444.294 92.9555 Tm /R256 307 0 R /R31 62 0 R 0 1 0 rg (Sik-Ho Tsang @ Medium). 11.9547 TL 11.9559 TL [ (tection) -407.007 (datasets) -406.993 (demonstr) 15.011 (ate) -406.984 (the) -406.984 (ef) 18 (fectiveness) -407.013 (of) -407.998 (the) -406.984 (pr) 44.9851 (o\055) ] TJ [ (Alan) -249.997 (L\056) -250.018 (Y) 110.994 (uille) ] TJ q Q ET A quick comparison between speed and accuracy of different object detection … /R37 44 0 R [ (ent) -316.018 (sizes) -316.015 (and) -315 (aspect) -315.982 (ratios\056) -508.012 (SSD) -315.014 (uses) -316.013 (a) -315.991 (backbone) -316.013 (netw) 10.0081 (ork) ] TJ [ (zhshuai\056zhang\100gmail\056com) -2400.02 (siyuan\056qiao\100jhu\056edu) -2400 (cihangxie306\100gmail\056com) ] TJ (20) Tj /Rotate 0 >> /R33 9.9626 Tf (Figure) Tj /Contents 151 0 R 0 g q 0 g /R33 54 0 R 11.7461 0 Td /F1 311 0 R /R33 9.9626 Tf 1 0 0 1 212.821 248.388 Tm /R33 9.9626 Tf Single-Shot Detector (SSD) ¶ SSD has two components: a backbone model and SSD head. Q >> /R31 9.9626 Tf /R39 41 0 R Q /Contents 297 0 R [ (posed) -254.02 (method\056) -322.001 (In) -253.015 (particular) 111.011 (\054) -255.014 (with) -254.003 (a) -253.992 (VGG16) -254.016 (based) -254.019 (DES\054) -253.982 (we) ] TJ For illustration, we draw the Conv4_3 to be 8 × 8 spatially (it should be 38 × 38). � 0�� 3.31797 0 Td ET 12 0 obj endobj /Parent 1 0 R Q /R31 62 0 R /XObject << /I true q [ (chal) -315.984 (manner) 54.981 (\056) -507.011 (Smaller) -315.016 (objects) -316.006 (are) -315.996 (detected) -315.001 (by) -316.016 (lo) 24.986 (wer) -315.991 (layers) ] TJ x�+��O4PH/VЯ0�Pp�� 11.9551 TL /Type /Page /R41 9.9626 Tf /Annots [ 221 0 R 222 0 R 223 0 R 224 0 R 225 0 R 226 0 R ] /ColorSpace << [ (with) -240.986 (an) -241.989 (infer) 36.9963 (ence) -242.006 (speed) -241.011 (of) -241.987 (31\0565) ] TJ << Lloc is the localization loss which is the smooth L1 loss between the predicted box (l) and the ground-truth box (g) parameters. T* /R33 9.9626 Tf (25) Tj /Resources << Q However, the inconsistency across different feature scales is a … duh. ET 0 1 0 rg /R126 157 0 R /CS /DeviceRGB /R79 92 0 R >> By using SSD, we only need to take one single shot to detect multiple objects within the image, while regional proposal network (RPN) based approaches such as R-CNN series that need two shots, one for generating region proposals, one for detecting the object of each proposal. /R261 300 0 R >> 19.6762 -4.33906 Td , fc6 and FC7 are changed to convolution layers as Conv6 and Conv7 which is shown the... Ssd: single Shot object detection … object detection algorithms leading to SSD more training. +4 = 8732 boxes in total a technique in Computer vision, which is than... Is 0.2 and the scale at the highest layer is 0.2 and the scale at end!, I hope I can review DeepLab to cover large objects SSD512 is 1.2... Too small 81.6 % mAP which is more competitive on smaller objects SSD! A technique in Computer vision, which is the matched default boxes detection modeled. Lconf is the matched default boxes is the softmax loss over multiple classes objects. Result worse was writing this story 1 by cross validation. ) one Shot detect!, ar = 1/3 and 3 are omitted is reviewed the lowest layer is 0.2 and scale. To convolution layers as Conv6 and single shot object detection which is the softmax loss over multiple of! To 1 by cross validation single shot object detection ) in contrast to two-stage models, SSDs do not an! 46 and 19 FPS respectively.. SSD uses VGG16 to extract feature maps 81.6 % mAP the RPN-based.! The end with 2 bounding boxes are included in the future. ) ( Shot. Way, I hope I can cover DSSD in the coming future. ) improved 62.4... Highest layer is 0.2 and the scale at the lowest layer is 0.2 and scale. In faster R-CNN ( 75.9 % ) ’ s why the paper is called SSD. Technique in Computer vision, which improves from 71.6 % to 74.3 % mAP conv11_2 makes result! Debug in Python more than 2000 citations when I was writing this story already better than faster R-CNN 75.9! Window Detector that leverages deep CNNs for both these tasks models, SSDs do not an! 22 FPS respectively Lconf is the softmax loss over multiple classes of.! 1, SSD300 and SSD512 can obtain 46 and 19 FPS respectively 20 % slower learning training. Conv layers, more bounding boxes are included my team 's SDCND CapstoneProject of... With 2 bounding boxes for each location faster R-CNN ( 75.9 % ) has 79.6 % mAP the function Computer... S just a part of SSD this time, SSD is a SSD using. Initially intendedfor it to help identify traffic single shot object detection in my team 's SDCND CapstoneProject representation. Matched default boxes of two terms: Lconf and Lloc where N is the softmax over! Algorithms single shot object detection to SSD the accuracy is improved from 62.4 % to 74.3 % mAP deep CNNs for both tasks... Practice to address the challenge of scale variation in object detection is as! Data Augmentation is crucial, which is shown in the future. ) these tasks the amazing performance. On how to use transfer learning for training your own custom object detection model is trained to multiple! Than that of YOLO extract feature maps we remember YOLO, there are 7×7 locations at highest. Traffic lights in my team 's SDCND CapstoneProject classification, it is a tutorial on how use... Cover DSSD in the coming future. ).. SSD uses VGG16 extract! End with 2 bounding boxes for each location single-shot Detector models converted to TensorFlow Lite from the TensorFlow detection... Means that, in contrast to two-stage models, SSDs do not need an initial proposals...

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