Infrared and Laser Engineering, Volume. 51, Issue 12, 20220253(2022)
Lightweight infrared dim vehicle target detection algorithm based on deep learning
Figures & Tables(22)
Fig. 1. Development trend of deep learning object detection algorithms in recent years
Fig. 9. Visible light image (left) and infrared image (right) of the same scene
Fig. 10. Structure diagram of Focus module after merging RDAB module
Fig. 11. Structure diagram of SPP module after merging RDAB module
Fig. 15. Image comparison of some detection results (left: actual annotation map, right: algorithm detection map)
Table 1. Analog image processing method for single image
View tableView in ArticleTable 1. Analog image processing method for single image
Flight live image Analog image processing Aircraft falling at high speed Image magnification under fixed field of view Aircraft level flight Image translation Aircraft rotation Image rotation at various angles Aircraft shaking Image translation Infrared imagers are affected by temperature and weather Image contrast, brightness changes Aerodynamic effect of aircraft flying at high speed Image random blur, edge blur Aircraft is disturbed Image random occlusion
Table 2. Distribution of infrared small and weak vehicle image dataset
View tableView in ArticleTable 2. Distribution of infrared small and weak vehicle image dataset
Target type [car] Target scene Desert, city, field, highway, village Data Augmentation Method Brightness change, contrast change, rotation, translation, scaling, flipping, clipping, splicing Original data Set 5023 (Before treatment) 4986 (After treatment) Data augmentation 1000 Training set 49228 Validation set 2590
Table 3. Specific parameters of the experimental en-vironment
View tableView in ArticleTable 3. Specific parameters of the experimental en-vironment
Experimental system Ubuntu18.04 CPU Inter Xeon Gold 6133 GPU NVIDIA TITAN RTX ×4 Memory 512 GB Development environment Python3.7 Deep learning framework Pytorch CUDA 10.2 cuDNN 7.5.3
Table 4. Comparison of experimental results between JH-YOLOv5-RDAB and typical algorithm networks
View tableView in ArticleTable 4. Comparison of experimental results between JH-YOLOv5-RDAB and typical algorithm networks
Algorithm Layers Parameters Size (Semi precision quantization) mAP50 GFLOPs Single test time/ms YOLOv3 261 61497430 123.4M 81.6 154.9 5.3 YOLOv4-tiny 99 5874116 23.6M 88.2 16.1 1.8 YOLOv4 488 63937686 256.3M 94.6 141.4 8.7 YOLOv5 s 283 7063542 14.4M 93.4 16.3 2.34 YOLOv5 m 391 21056406 42.5M 94.5 50.3 3.45 YOLOv5 l 499 46631350 93.7M 95.2 114.1 4.93 YOLOv5 x 607 87244374 175.1M 95.9 217.1 8.43 YOLO-Fastest 277 356700 4.8M 32.1 0.96 1.6 JH-YOLOv5-RDAB 565 3117313 6.6M 95.1 8.4 2.52
Table 5. Comparison of experimental effects of lightweight tailoring based on YOLOv5 s
View tableView in ArticleTable 5. Comparison of experimental effects of lightweight tailoring based on YOLOv5 s
Algorithm Depth_ multiple width_ multiple Performance effect/times Network size/times YOLOv5 x 1.33 1.25 2.3 12.2 YOLOv5 l 1 1 1.8 6.5 YOLOv5 m 0.67 0.75 1.2 2.9 YOLOv5 s 0.33 0.50 1 1 Net 1 0.25 0.33 0.85 0.49 Net 2 0.2 0.25 0.63 0.35 Net 3 0.1 0.2 0.33 0.12
Table 6. Comparison of network experimental effects based on attention mechanism fusion
View tableView in ArticleTable 6. Comparison of network experimental effects based on attention mechanism fusion
Fused operator Integrated ECA Integrated EPA Performance effect/times Network size/times Performance effect/times Network size/times Net 2 1 1 1 1 CBL 1.1 1.6 1.3 2.4 SPP 1.2 1.12 1.43 1.18 CSP 1.3 1.1 1.33 1.11 Focus 1.02 1.01 1.11 1.01
Table 7. Comparison of network experimental effects based on RDAB
View tableView in ArticleTable 7. Comparison of network experimental effects based on RDAB
Fused operator Integrated RDAB Performance effect/times Network size/times Focus 1.28 1.06 SPP+ Focus 1.35 1.08
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Renhao Cai, Ning Cheng, Zhiyong Peng, Shize Dong, Jianmin An, Gang Jin. Lightweight infrared dim vehicle target detection algorithm based on deep learning[J]. Infrared and Laser Engineering, 2022, 51(12): 20220253
Paper Information
Category: Image processing
Received: Mar. 20, 2022
Accepted: --
Published Online: Jan. 10, 2023
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