Lightweight Target Detection Network Integrating Scene Context

Table 1. Hyperparameters of pre-trained model
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Table 1. Hyperparameters of pre-trained model
Weight decay Batch size Learning rate Momentum Maximum iteration
0.0005 16 0.001 (M<1000)0.0001(M>1000) 0.9 10000

Table 2. Results of different target detection algorithms
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Table 2. Results of different target detection algorithms
Algorithm Backbone mAP /% FPS /(frame·s^-1)
Fast R-CNN VGG16 70.0 6
Faster R-CNN VGG16 75.8 8
YOLO 64.3 18
SSD₃₀₀ VGG16 73.4 39
SSD₅₀₀ VGG16 74.9 22
YOLOv2 Darknet-19 74.3 34
YOLOv3 Darknet-53 75.9 27.6
SS-YOLOv3 MobileNetV3 84.3 33.4

Table 3. Detection results of YOLOv3 and SS-YOLOv3 unit: %
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Table 3. Detection results of YOLOv3 and SS-YOLOv3 unit: %
Class YOLOV3 SS-YOLOv3 Subtract
Aeroplane 74.6 84.9 10.3
Car 75.9 84.0 8.1
Boat 75.7 85.6 9.9
Cycle 76.3 82.4 6.1
House 74.3 84.6 10.3
Electrocar 76.9 82.7 5.8
Person 77.6 85.9 8.3

Table 4. Results of ablation learning
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Table 4. Results of ablation learning
YOLOv3 MobileNetV3 Scene SRU CIOU mAP /% FPS /(frame·s^-1)
√ 75.9 27.6
√ √ 74.2 36.4
√ √ √ 83.5 33.4
√ √ √ √ 84.3 33.4

Table 5. Detect results of YOLOv3 and SS-YOLOv3 unit: %
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Table 5. Detect results of YOLOv3 and SS-YOLOv3 unit: %
Algorithm Pedestrian Person Bicycle Car Van Truck Tricyle Awn Bus Motor
YOLOv3 20.75 8.04 7.43 45.82 30.56 22.89 13.04 9.27 33.84 11.46
SS-YOLOv3 22.45 7.93 9.65 51.03 34.54 24.13 15.02 9.13 36.89 13.73

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Tingting Liu, Hua Miao, Lin Li, Yang Xiang, Qi Li, Qi Meng. Lightweight Target Detection Network Integrating Scene Context[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2010005

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