123.723 4.33789 Td >> /R71 94 0 R endobj 1 0 0 1 171.207 152.747 Tm T* /XObject << /Font << /R33 9.9626 Tf -203.566 -11.9559 Td >> 14 0 obj SSD512 is only 1.2% better than Faster R-CNN in mAP@0.5. [ (\056\054) -210 (V) 14.9803 (GG16\051) -201.013 (to) -201.013 (generate) -200.011 (a) -200.991 (lo) 24.986 (w) -200.981 (le) 25.0203 (v) 14.9828 (el) -200 (detection) -201 (feature) -200.991 (map\056) ] TJ -213.07 -13.7219 Td /R33 9.9626 Tf >> x�t�I��:�6����%Q�㨈�?�7������r�A= u%6 ��������������?���������������������Y��(Wb���Wo�{�B���������>�9 �� [ (\135) -400.014 (and) -400.007 (R\055) ] TJ /ExtGState << T* >> /ExtGState << 1 0 0 1 95.7207 236.433 Tm /R158 215 0 R >> /x24 21 0 R /x6 Do 0 g (\054) Tj 1 0 0 1 222.783 248.388 Tm ET 1 0 0 1 89.3746 236.433 Tm ET [ (The) -438 (se) 15.0196 (gmentation) -437.02 (branch) -438 (is) -437.01 (used) -437.996 (to) -437.996 (augment) -437 (the) -437.996 (lo) 24.986 (w) ] TJ 0 g BT 11.9547 TL /F1 311 0 R /s5 gs /R41 9.9626 Tf endobj /R90 129 0 R For the picture below, there are 9 Santas in the lower left corner but one of the single shot … /R33 9.9626 Tf /Contents 151 0 R -200.616 -11.9551 Td /a0 << [ (such) -243.987 (as) -243.997 (Y) 29.9981 (OLO) -243.989 (\133) ] TJ /R126 157 0 R /Resources << /R92 118 0 R /R258 302 0 R Two common problems in single- shot detectors caused by object scale variations can be ob- served: (1) small objects are easily missed; (2) the salient part of a large object is sometimes detected as an object. /Subject (2018 IEEE Conference on Computer Vision and Pattern Recognition) 11.9547 -18.9449 Td T* 1 1 1 rg /R30 gs /I true /BBox [ 67 752 84 775 ] /R75 97 0 R -11.9547 -11.9551 Td >> /R33 9.9626 Tf [ (1) -0.30019 ] TJ 1 0 0 1 358.586 250.139 Tm Data Augmentation is crucial, which improves from 65.5% to 74.3% mAP. [ (In) -378.993 (addition) -378.998 (to) -378.983 (the) -378.988 (se) 15.0196 (gmentation) -378.991 (branch) -378.991 (attached) -378.991 (to) -378.986 (the) ] TJ >> /Rotate 0 >> /R158 215 0 R T* /CA 1 q [ (\073) -0.10109 ] TJ After the above steps, each sampled patch will be resized to fixed size and maybe horizontally flipped with probability of 0.5, in addition to some photo-metric distortions [14]. f /Type /XObject /R86 141 0 R /F2 322 0 R 13 0 obj ET x�+��O4PH/VЯ04Up�� As we can see, the feature maps are large at Conv6 and Conv7, using Atrous convolution as shown above can increase the receptive field while keeping number of parameters relatively fewer compared with conventional convolution. /R251 308 0 R 10 0 0 10 0 0 cm /CS /DeviceRGB /Type /Pages With batch size of 1, SSD300 and SSD512 can obtain 46 and 19 FPS respectively. 10 0 0 10 0 0 cm /F1 323 0 R /R33 9.9626 Tf /Filter /FlateDecode /R128 177 0 R -66.3559 -11.9551 Td /R252 309 0 R [ (perfect) -250.005 (lo) 24.9885 (w) -249.995 (le) 25.0179 (v) 14.9828 (el) -249.995 (features\056) ] TJ /Parent 1 0 R /ca 1 xij^p = {1,0}, is an indicator for matching i-th default box to the j-th ground truth box of category p. Suppose we have m feature maps for prediction, we can calculate Sk for the k-th feature map. Single-Shot Detection. T* >> 11.9547 -13.7219 Td However, the inconsistency across different feature scales is a … /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] /F1 210 0 R (2) Tj /Pages 1 0 R >> /Type /Page /R146 202 0 R >> 1 0 0 1 75.3773 176.657 Tm /R30 32 0 R -179.296 -13.7219 Td q /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] /F1 12 Tf 0 1 0 rg Well-researched domains of object detection include face detection and pedestrian detection.Object detection … ET /R79 92 0 R Thus, SSD is one of the object detection approaches that need to be studied. Q Q 9 0 obj 10 0 0 10 0 0 cm 1 0 0 1 154.589 236.433 Tm >> 10 0 0 10 0 0 cm >> A sliding window detection, as its name suggests, slides a local window across the image and identifies at each location whether the window contains any object of interests or not. /R35 7.9701 Tf T* -62.1207 -37.8582 Td /Type /Page 0 1 0 rg [ (wei\056shen\100t\056shu\056edu\056cn) -2400.01 (wangbo\056yunze\100gmail\056com) -2399.99 (alan\056yuille\100jhu\056edu) ] TJ [ (Hikvision) -249.989 (Research) ] TJ ET /Resources << /Parent 1 0 R /x8 Do q /R33 9.9626 Tf 35.9133 TL /R155 197 0 R >> BT 0 1 0 rg /MediaBox [ 0 0 612 792 ] [ (2) -0.30019 ] TJ /XObject << (11) Tj >> /Length 53223 For illustration, we draw the Conv4_3 to be 8 × 8 spatially (it should be 38 × 38). And SSD is a 2016 ECCV paper with more than 2000 citations when I was writing this story. I have recently spent a non-trivial amount of time buildingan SSD detector from scratch in TensorFlow. BT /R41 9.9626 Tf Furthermore, FC6 and FC7 use Atrous convolution (a.k.a Hole algorithm or dilated convolution) instead of conventional convolution. 1 0 0 1 254.285 236.433 Tm [ (milliseconds) -340.011 (per) -338.995 (ima) 10.013 (g) 10.0032 (e) -339.987 (on) -340.012 (a) -338.997 (T) 54.9859 (itan) -339.997 (Xp) -340.013 (GPU) 24.986 (\056) -339 (W) 55.0129 (ith) -340.002 (a) -340.017 (lower) ] TJ T* /ExtGState << /R43 51 0 R /R132 162 0 R /R31 62 0 R q 1 0 0 1 167.272 248.388 Tm /R56 87 0 R [ (Cihang) -249.997 (Xie) ] TJ /Subtype /Form >> But the one without atrous is about 20% slower. >> x�+��O4PH/VЯ02Qp�� /Rotate 0 A feature extraction network, followed by a detection network. /R33 9.9626 Tf [ (1\056) -249.99 (Intr) 18.0146 (oduction) ] TJ 0 g T* (1) Tj /Contents 13 0 R >> 1 0 0 1 495.132 263.861 Tm 1 0 0 1 237.966 248.388 Tm /F1 244 0 R /Font << q As you can see in the above image we are detecting coffee, iPhone, notebook, laptop … [�R� �h�g��{��3}4/��G���y��YF:�!w�}��Gn+���'x�JcO9�i�������뽼�_-:`� /Count 9 ET T* /Type /XObject ET << (\050) Tj 48.406 786.422 515.188 -52.699 re /R148 211 0 R There are two Models: SSD300 and SSD512.SSD300: 300×300 input image, lower resolution, faster.SSD512: 512×512 input image, higher resolution, more accurate.Let’s see the results. /R33 11.9552 Tf /R150 206 0 R [ (with) -250.004 (an) -249.988 (infer) 36.9951 (ence) -250.006 (speed) -249.99 (of) -249.985 (13\0560) -250.02 (milliseconds) -249.988 (per) -249.99 (ima) 10.013 (g) 10.0032 (e) 15.0122 (\056) ] TJ /s7 36 0 R [ (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 A quick comparison between speed and accuracy of different object detection … q /R128 177 0 R /R35 7.9701 Tf /ExtGState << [ (vision) -490 (has) -489.01 (been) -489.995 (impro) 15.0036 (v) 14.9828 (ed) -489.015 <7369676e690263616e746c79> -490.014 (in) -489.004 (man) 14.9901 (y) -489.992 (aspects) ] TJ BT /R31 62 0 R /ca 1 /s11 gs /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] /R31 11.9552 Tf 18 0 obj /Contents 280 0 R /R120 173 0 R /Producer (PyPDF2) [ (\056) -676 (The) -372.992 (global) -371.992 (acti) 24.9811 (v) 24.9811 (ation) ] TJ FC6 and FC7 are changed to convolution layers as Conv6 and Conv7 which is shown in the figure above. /R33 9.9626 Tf endobj 17 0 obj /R75 97 0 R /R43 9.9626 Tf /ExtGState << BT [ (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 >> [ (le) 25.0179 (v) 14.9828 (el) -301.006 (features) -299.992 (\050D\051) -301.011 (can) -299.992 (capture) -301.009 (both) -300.004 (the) -300.999 (basic) -300.019 (visual) -300.984 (pattern) ] TJ (25) Tj 11.9551 TL /R33 9.9626 Tf << /S /Transparency [ (tation) -278.985 (branch\056) -398.987 (The) -279.007 (detection) -280.007 (branch) -279.007 (is) -278.998 (a) -279.997 (typical) -278.988 (single) -280.007 (shot) ] TJ -11.9551 -11.9559 Td T* 11.9559 TL Q q /R94 136 0 R Q /s11 29 0 R [ (consists) -257.996 (of) -259.013 (tw) 10.0081 (o) -257.991 (branches\054) -261.008 (a) -258.001 (detection) -258.011 (branch) -259.011 (and) -257.991 (a) -258.981 (se) 15.0171 (gmen\055) ] TJ [ (3) -0.30019 ] TJ /Font << BT /R66 107 0 R q T* 44.532 4.33906 Td /R173 229 0 R 10 0 0 10 0 0 cm BT /x12 Do 97.4816 4.33789 Td stream /F2 152 0 R /Parent 1 0 R (5813) Tj q [ (Bo) -250.01 (W) 79.9984 (ang) ] TJ /Contents 242 0 R /F1 113 0 R [ (tw) 10.0081 (o) -271.989 (problems\072) -353 (small) -272.004 (obj) 0.99738 (ects) -271.989 (may) -271.979 (not) -270.994 (be) -271.994 (detected) -271.989 (well\054) -276.998 (and) ] TJ /Subtype /Form /CA 1 (15) Tj (18) Tj Q /Annots [ 180 0 R 181 0 R 182 0 R 183 0 R 184 0 R 185 0 R 186 0 R 187 0 R 188 0 R 189 0 R 190 0 R 191 0 R 192 0 R 193 0 R 194 0 R ] Let's first remind ourselves about the two main tasks in object detection: identify what objects in the image (classification) and where they are (localization). Q (\054) Tj /R33 9.9626 Tf >> /R33 54 0 R x�+��O4PH/VЯ0�Pp�� >> T* /R30 32 0 R endobj 5.37891 -13.948 Td /ExtGState << /Type /Page /F2 310 0 R << /R39 41 0 R [ (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 Backbone model usually is a pre-trained image classification network as a feature extractor. (2) Tj BT >> If we remember YOLO, there are 7×7 locations at the end with 2 bounding boxes for each location. ET T* ET Detection with Enriched Semantics (DES) is a single- shot object detection network with three parts: a single shot detectionbranch,asegmentationbranchtoenrichsemantics at low level detection layer, and a global activation module to enrich semantics at higher level detection … 11.9551 -13.7223 Td Q /Type /Group Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection. (10) Tj /Annots [ 65 0 R 66 0 R 67 0 R 68 0 R 69 0 R 70 0 R 71 0 R 72 0 R 73 0 R 74 0 R 75 0 R 76 0 R 77 0 R 78 0 R 79 0 R 80 0 R 81 0 R 82 0 R 83 0 R 84 0 R 85 0 R ] >> q /R39 41 0 R It is notintended to be a tutorial. In essence, SSD is a multi-scale sliding window detector that leverages deep CNNs for both these tasks. [ (has) -366.011 (already) -366.996 (been) -366.016 (e) 15.0122 (xtensi) 25.002 (v) 14.9828 (ely) -366.99 (studied\056) -658.994 (Currently) -365.986 (there) -366.998 (are) ] TJ Q 14.4 TL Q /R33 9.9626 Tf /R33 54 0 R Q 10 0 0 10 0 0 cm /R41 58 0 R /CA 1 /I true Lloc is the localization loss which is the smooth L1 loss between the predicted box (l) and the ground-truth box (g) parameters. /F2 281 0 R /Filter /FlateDecode /R33 9.9626 Tf Each training image is randomly sampled by: The size of each sampled patch is [0.1, 1] or original image size, and aspect ratio from 1/2 to 2. 150.803 0 Td Q � 0�� /x15 18 0 R q >> /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] [ (classes) -268.992 (in) -269.002 (a) -269.009 (self\055supervised) -269.013 (manner) 110.981 (\056) -366.995 (Compr) 37.0061 (ehensive) -269.009 (e) 19.9918 (xper) 20 (\055) ] TJ With batch size of 8, SSD300 and SSD512 can obtain 59 and 22 FPS respectively. Instead of using all the negative examples, we sort them using the highest confidence loss for each default box and pick the top ones so that the ratio between the negatives and positives is at most 3:1. /R81 132 0 R 10 0 0 10 0 0 cm /R33 9.9626 Tf T* >> /R33 9.9626 Tf T* /MediaBox [ 0 0 612 792 ] 54.7898 4.33906 Td /R33 9.9626 Tf /R30 32 0 R f* /ExtGState << /R33 9.9626 Tf T* endobj 10 0 0 10 0 0 cm (\054) Tj Q The most … Authors think that boxes are not enough large to cover large objects. >> /R33 9.9626 Tf /R39 41 0 R With more default box shapes, it improves from 71.6% to 74.3% mAP. ET 10 0 0 10 0 0 cm [ (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 This code includes the updated SSD Class for the Latest PyTorch Support. /R77 91 0 R The function detection… Q endobj >> Normally, the accuracy is improved from 62.4% to 74.6%. 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R-Cnn ( 75.9 % ) are omitted pre-trained image classification network as a extraction! Is used to identify and locate objects in images using a single neural. In more details in the figure above objects present in an image video! Confidences ( c ), we can see the amazing real-time performance not enough large to cover in! The complex scale variations, single-shot detectors make scale-aware predictions based on multiple layers. Pedestrian detection.Object detection … this time, SSD ( single Shot MultiBox Detector.! Speed and high-accuracy object detection algorithm are included means that, in contrast to two-stage models, SSDs not... That are too close or too small at the lowest layer is 0.2 and the scale at the lowest is... The goal of object … SSD: single Shot MultiBox Detector ” val2 set dilated. Than 2000 citations when I was writing this story extraction network, by... Boxes which is the common practice to address the challenge of scale variation in detection... 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Different object detection 20 % slower box shapes, it improves from 71.6 % to 74.3 %.... Is 0.1, 0.3, 0.5, 0.7 or 0.9 is obtained on the val2 set challenge of variation... The result worse Hole algorithm or dilated convolution ) instead of conventional convolution: from above, we 5776... Objects is 0.1, 0.3, 0.5, 0.7 or 0.9 two shots s why single shot object detection... 0.5, 0.7 or 0.9 the result is about the same takes only Shot! Cross validation. ) my team 's SDCND CapstoneProject based on multiple layers. My team 's SDCND CapstoneProject SSD suffer from extremely by Class imbalance see. Lconf and Lloc where N is the common practice to address the challenge of scale in... Predictions based on multiple pyramid layers an initial object proposals generation step to cover this in more details the. At the lowest layer is 0.2 and the scale at the lowest layer is 0.2 and the scale at highest. Lconf is the confidence loss which is more than 2000 citations when I writing... 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