Recognition of Wild Animals Using Infrared Camera Images Based on YOLOv5

Table 1. Depth comparison of four network structures
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Table 1. Depth comparison of four network structures
Residual componentmodel YOLOv5s YOLOv5m YOLOv5l YOLOv5x
CSP1_1_A 1 2 3 4
CSP1_3_B 3 6 9 12
CSP1_3_C 3 6 9 12
CSP2_1_A 1 2 3 4
CSP2_1_B 1 2 3 4
CSP2_1_C 1 2 3 4
CSP2_1_D 1 2 3 4
CSP2_1_E 1 2 3 4

Table 2. Width comparison of four network structures
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Table 2. Width comparison of four network structures
Filter model YOLOv5s YOLOv5m YOLOv5l YOLOv5x
Focus 32 48 64 80
CBL_A 64 96 128 160
CBL_B 128 192 256 320
CBL_C 256 384 512 640
CBL_D 512 768 1024 1280

Table 3. Number of images in each class
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Table 3. Number of images in each class
Class Train Val Test Total
Sika deer 650 73 93 816
Tufted deer 538 54 63 655
Wild boar 571 74 72 717
Impala 567 71 69 707
Tragopan temminckii 401 32 40 473
Total 2727 304 337 3368

Table 4. Comparison of experimental results of each model on the test set
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Table 4. Comparison of experimental results of each model on the test set
Model F1-score mAP Model size /MB Inference time /ms
YOLOv5s 0.917 0.965 14 9.5
YOLOv5m 0.91 0.961 42 12.9
YOLOv5l 0.908 0.96 93 17.9
YOLOv5x 0.918 0.965 170 21.7

Table 5. Statistics of number of false and missed detections
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Table 5. Statistics of number of false and missed detections
Class YOLOv5s YOLOv5m YOLOv5l YOLOv5x
Sika deer 3 6 2 2
Tufted deer 12 9 12 10
Boar 0 0 0 2
Impala 12 4 4 3
Tragopan temminckii 1 1 3 1
Empty prediction box 0 0 0 2
Total 28 20 21 21

Table 6. Comparison of YOLOv5m and YOLOv3-tiny
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Table 6. Comparison of YOLOv5m and YOLOv3-tiny
Model F1-score mAP Weight /MB Inference time /ms
YOLOv5m 0.91 0.961 42 12.9
YOLOv3-tiny 0.842 0.943 16.7 3.7

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Minglun Yang, Xu Zhang, Ying Guo, Xinwen Yu, Yanan Hou, Jiajun Gao. Recognition of Wild Animals Using Infrared Camera Images Based on YOLOv5[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215015

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