Chinese Optics, Volume. 17, Issue 6, 1281(2024)
Weak feature confocal channel regulation for underwater sonar target detection
Figures & Tables(13)
Fig. 2. Structural diagram of global information aggregation module
Fig. 3. Structural diagram of confocal channel regulation pooling module
Table 1. Correspondence between output branches and a priori boxes before and after improvement
View tableView in ArticleTable 1. Correspondence between output branches and a priori boxes before and after improvement
改进前 改进后 输出 分支 先验框 尺度 输出 分支 先验框 尺度 - - P4 [(116,90),(156,198),(373,326)] N3 [(10,13),(16,30),(33,23)] P3 [(5,6),(8,14),(15,11)] N4 [(30,61),(62,45),(59,119)] P2 [(10,13),(16,30),(33,23)] N5 [(116,90),(156,198),(373,326)] P1 [(30,61),(62,45),(59,119)]
Table 2. Target image area percentage
View tableView in ArticleTable 2. Target image area percentage
Classes name Maximum area of target frame(height×width) Ration ball 156×142 0.04 circle cage 128×148 0.04 cube 140×164 0.04 cylinder 122×102 0.02 human body 215×216 0.09 metal bucket 168×206 0.07 plane 187×278 0.10 square cage 130×118 0.03 tyre 166×166 0.05
Table 3. Improved module ablation experiments
View tableView in ArticleTable 3. Improved module ablation experiments
GFLOPS mAP50 human body ball circle cage square cage tyre metal bucket cube cylinder plane Baseline 15.8 77.8 81.2 84.9 81.6 78.7 64.8 61.1 84.4 71.8 92.5 +特征激活 18.6 80.1 87.6 82.5 81.1 80.9 64.9 68.8 86.4 77.5 91 +GIAM 19.5 80.9 86.8 83.8 84.1 78.5 64.3 80.1 85.8 74.8 90.3 +CFCRP 23.7 83.3 89.8 85.7 79.1 82.9 73.1 85.1 91.7 71.2 91
Table 4. Correspondence of experiment codes with output layers and a priori boxes
View tableView in ArticleTable 4. Correspondence of experiment codes with output layers and a priori boxes
实验代号 输出分支 先验框尺度 输出分支 先验框尺度 G1 - - N4 [(30,61),(62,45),(59,119)] N3 [(10,13),(16,30),(33,23)] N5 [(116,90),(156,198),(373,326)] G2 P4 [(5,6),(8,14),(15,11)] P2 [(30,61),(62,45),(59,119)] P3 [(10,13),(16,30),(33,23)] P1 [(116,90),(156,198),(373,326)] G3 P4 [(10,13),(16,30),(33,23)] P2 [(30,61),(62,45),(59,119)] P3 [(5,6),(8,14),(15,11)] P1 [(116,90),(156,198),(373,326)] G4 P4 [(30,61),(62,45),(59,119)] P2 [(10,13),(16,30),(33,23)] P3 [(5,6),(8,14),(15,11)] P1 [(116,90),(156,198),(373,326)] G5 P4 [(116,90),(156,198),(373,326)] P2 [(10,13),(16,30),(33,23)] P3 [(5,6),(8,14),(15,11)] P1 [(30,61),(62,45),(59,119)]
Table 5. Results of quantitative experiments on the combination of detection branch and a priori frame
View tableView in ArticleTable 5. Results of quantitative experiments on the combination of detection branch and a priori frame
实验代号 GFLOPS mAP50 human body ball circle cage square cage tyre metal bucket cube cylinder plane G1 15.8 77.8 81.2 84.9 81.6 78.7 64.8 61.1 84.4 71.8 92.5 G2 15.9 78.6 88.8 83.7 79.7 82.6 60.6 66.9 87.1 68.5 89.7 G3 15.9 77.5 77.9 82.3 80.7 72.4 63.9 67.6 86.9 73.7 92.1 G4 15.9 79.2 79.9 81.7 78.5 75.2 64.4 74.2 87.3 76.6 95.1 G5 15.9 79.3 89.4 83.2 78.6 83.3 63 71.1 84.3 71.2 90
Table 6. Multi-scale confocal convolution comparison experiments
View tableView in ArticleTable 6. Multi-scale confocal convolution comparison experiments
Base Model 共焦卷积核 GFLOPS mAP50 human body ball circle cage square cage tyre metal bucket cube cylinder plane YOLOv5s +特征激活 +GIAM - 19.5 80.9 86.8 83.8 84.1 78.5 64.3 80.1 85.8 74.8 90.3 +1×1 19.3 80.8 85.3 86.1 67.3 85.1 71.3 84.5 89.7 68.8 89.5 +1×1 +3×3 19.4 81.4 89.4 84.1 78.8 83.3 66.1 87.5 87.1 68.7 87.5 +1×1 +3×3 +5×5 20.8 80.6 91.8 83.4 75 79.1 67.2 72.3 87.2 80.5 88.8 +1×1 +3×3 +5×5 +7×7 23.7 83.3 89.8 85.7 79.1 82.9 73.1 85.1 91.7 71.2 91 +1×1 +3×3 +5×5 +7×7 +9×9 27.6 78.2 85.3 84.9 74 80.2 66.4 78.2 88.6 61.6 84.6
Table 7. Spatial feature pyramid pooling comparison experiments
View tableView in ArticleTable 7. Spatial feature pyramid pooling comparison experiments
Base Model 空间特征金字塔池化 GFLOPS mAP50 human body ball circle cage square cage tyre metal bucket cube cylinder plane YOLOv5s +特征激活 +GIAM +SPPF 19.5 80.9 86.8 83.8 84.1 78.5 64.3 80.1 85.8 74.8 90.3 +SPP 18.5 81.4 86.1 83.6 81.9 86.8 67.5 73.2 86.4 76.8 90.2 +SPPFCSPC 23.6 73.9 78.1 79.7 71.6 76.5 60.3 60.4 85.4 62.1 91.2 +ASPP 25.1 77 78.4 80.1 72.8 77.6 70 76.6 88 61.8 87.9 +RFB 19.0 80.5 86.8 85.8 78.7 80.4 69.7 80.3 87.2 72.2 83.8 +SPPCSPC 23.6 75.2 80.3 78.6 73.4 72.8 62.4 71.4 82.4 72.3 83.1 +CFCRP 23.7 83.3 89.8 85.7 79.1 82.9 73.1 85.1 91.7 71.2 91
Table 8. Network model comparison experiment
View tableView in ArticleTable 8. Network model comparison experiment
Model GFLOPS mAP50 human body ball circle cage square cage tyre metal bucket cube cylinder plane SSD[ 38 ]347.1 78.8 89.1 90.9 74.8 76.4 62.3 77.4 81 68.4 89 RetinaNet[ 39 ]207.9 68.4 89.8 77.5 59.8 74.7 47.4 75.9 78.6 24 87.9 YOLOv5x[ 40 ]203.9 77.4 76.1 83.8 76.8 72.7 62.3 80 83.8 66.2 95 Faster RCNN[ 41 ]193.8 71.2 80.4 81.1 77.6 75.7 45 70 81.8 40.4 88.7 YOLOv7[ 29 ]103.3 60.4 71.5 74.2 57.8 67 48.1 59.8 72.8 35.8 56.4 YOLOv5m[ 40 ]48 71.3 68.6 81.8 79 61.5 56.7 58.9 85.1 69.3 80.7 DAMO-YOLO[ 42 ]36 72.7 65.5 81.3 70.2 70.8 38.7 87.8 85.6 71.8 82.2 YOLOv8s[ 43 ]28.2 81.4 82.3 86.5 81.1 86.6 65 93.7 88.5 53.5 95 YOLOXs[ 44 ]26.7 82.4 88.1 88 75.9 87.2 71.7 79.3 89.6 71.4 90 YOLOv5s[ 40 ]15.8 77.8 81.2 84.9 81.6 78.7 64.8 61.1 84.4 71.8 92.5 YOLOv7-tiny[ 45 ]13.1 64.2 70.3 84.5 46.7 76.8 35.4 66.3 83.3 41.7 72.7 YOLOv3-tiny[ 41 ]5.57 63.2 65.2 73.6 69.4 63.2 37.5 63.9 76 48 72.1 YOLOv5n[ 40 ]4.2 72.3 86 79.2 74.5 77.9 59.3 52.8 81.8 58 80.8 WFCCMNet 23.7 83.3 89.8 85.7 79.1 82.9 73.1 85.1 91.7 71.2 91
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Meng-yun HE, Zi-fen HE, Yin-hui ZHANG, Guang-chen CHEN, Feng ZHANG. Weak feature confocal channel regulation for underwater sonar target detection[J]. Chinese Optics, 2024, 17(6): 1281
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Received: Feb. 5, 2024
Accepted: Apr. 26, 2024
Published Online: Jan. 14, 2025
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