Chinese Optics, Volume. 17, Issue 5, 1087(2024)
Self-supervised learning enhancement and detection methods for nocturnal animal images
Figures & Tables(12)
Fig. 7. Performance comparison of various advanced image enhancement algorithms
Fig. 8. Visualization results of enhanced and unenhanced target detection
Table 1. Evaluation results of different algorithms
View tableView in ArticleTable 1. Evaluation results of different algorithms
算法 PSNR SSIM MAE Runtime（s） 无监督 自监督 Zero-DCE ++ 27.93 0.5725 44.9393 0.0008 SCI 27.90 0.5254 48.7550 0.000 6 Zero-Denoise（ours） 28.53 0.766 5 26.151 6 0.0181 有监督 StableLLVE 27.92 0.7373 32.4789 0.5900 URetinex-Net 28.45 0.833 2 21.156 2 0.0367 Retinexnet 28.06 0.4250 32.0174 0.1200 MBLLEN 28.04 0.7247 31.2498 8.5633 Zero-Denoise（ours） 28.53 0.7665 26.1516 0.018 1
Table 2. Experimental results of improved YOLOv8 ablation
View tableView in ArticleTable 2. Experimental results of improved YOLOv8 ablation
算法 改进主干 改进颈部 改进损失 P R mAP@0.5 mAP@0.5:0.95 YOLOv8 72.3 70.8 80.7 49.7 A √ 73.3(+1.0%) 72.3(+1.5%) 81.2(+0.5%) 50.3(+0.6%) B √ 75.0(+2.7%) 74.6(+3.8%) 81.4(+0.7%) 50.4(+0.7%) C √ 73.3 72.3(+1.5%) 80.9(+0.2%) 50.1(+0.4%) D √ √ 76.0(+3.7%) 74.9(+4.1%) 81.9(+1.2%) 51.3(+1.4%) Ours √ √ √ 77.9(+5.6%) 75.2(+5.6%) 82.2(+1.5%) 51.7+(2.0%)
Table 3. The effect of different algorithms on improved YOLOv8
View tableView in ArticleTable 3. The effect of different algorithms on improved YOLOv8
算法 Rabbit Bird Chicken Mouse Duck mAP@0.5 Im-YOLOv8 94.0 86.5 62.2 80.3 63.5 82.2 URetinex-Net+Im-YOLOv8 94.5 88.0 73.5 91.9 66.2 83.5(+1.3%) StableLLVE+Im-YOLOv8 93.5 90.6 71.6 82.2 64.5 82.5(+0.3%) SCI+Im-YOLOv8 94.7 91.1 72.0 93.2 69.5 84.7(+2.5%) Retinexnet+Im-YOLOv8 89.2 76.6 60.9 68.5 58.7 78.5(-3.7%) MBLLEN+Im-YOLOv8 94.2 90.8 72.5 89.0 65.5 83.1(+1.1%) Zero-Denoise+Im-YOLOv8 95.0 91.7 74.6 97.4 76.0 87.8(+5.6%)
Table 4. Visualization results comparison of enhanced and unenhanced target detection
View tableView in ArticleTable 4. Visualization results comparison of enhanced and unenhanced target detection
组别 应检个数/实检个数 检测精度 结论 a鸽类增强后检测结果 3/3 0.49/0.79/0.89 未增强图像出现漏检数量1，阴影处鸽类未检出 b鸽类未增强检测结果 3/2 0.00/0.81/0.88 c鸡类增强后检测结果 6/6 0.55/0.80/0.81/0.85/0.57/0.46 未增强图像出现漏检数量2，角落处和被遮挡的鸡类未检出 d鸡类未增强检测结果 6/4 0.00/0.84/0.89/0.83/0.85/0.00 e鸭类增强后检测结果 3/3 0.50/0.86/0.81 未增强图像出现漏检数量1，深处的鸭类未检出 f鸭类未增强检测结果 3/2 0.00/0.71/0.68 g鸭类增强后检测结果 4/4 0.27/0.66/0.82/0.36 未增强图像出现漏检数量2，左侧和右侧阴影内鸭类未检出 h鸭类未增强检测结果 4/2 0.00/0.31/0.88/0.00 i鼠类增强后检测结果 4/4 0.87/0.82/0.88/0.80 增强图像中鼠类检测精度提升明显，从0.27、0.26提升至0.82、0.80 j鼠类未增强检测结果 4/4 0.83/0.27/0.87/0.26 k兔类增强后检测结果 1/1 0.90 增强图像中兔类检测精度提升明显，从0.77提升至0.90 l兔类未增强检测结果 1/1 0.77
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Chi WANG, Chen SHEN, Qing HUANG, Guo-feng ZHANG, Han LU, Jin-bo CHEN. Self-supervised learning enhancement and detection methods for nocturnal animal images[J]. Chinese Optics, 2024, 17(5): 1087
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Received: Jan. 11, 2024
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Published Online: Dec. 31, 2024
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