夜间动物图像自监督学习增强与检测方法

王驰; 沈晨; 黄庆; 张国峰; 卢汉; 陈金波

doi:10.37188/CO.2024-0011

夜间动物图像自监督学习增强与检测方法

doi: 10.37188/CO.2024-0011

王驰^1,,
沈晨¹,
黄庆²,
张国峰¹,
卢汉¹,
陈金波^1, ,

1.
上海大学机电工程与自动化学院, 上海 200444
2.
中国航空工业集团公司洛阳电光设备研究所, 洛阳 471023

基金项目: 国家自然科学基金项目（No. 62175144）；北京市航空智能遥感装备工程技术研究中心开放基金课题（No. AIRSE20233）

详细信息

作者简介:
王　驰（1982—），男，河南周口人，博士（后），教授，2009 年于天津大学获得博士学位，现为上海大学机电工程与自动化学院教师，主要从事精密测试技术及仪器等方面的研究。 E-mail：wangchi@shu.edu.cn

陈金波（1980—），男，内蒙古呼和浩特人，博士，工程师，2002年于上海大学获得硕士学位，2014年于上海大学获得博士学位，现为上海大学机电工程与自动化学院教师，主要从事机器视觉与检测方面的研究。E-mail：jbchen@shu.edu.cn

中图分类号: TP394.1;TH691.9
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出版历程
- 网络出版日期: 2024-06-17

Self-supervised learning enhancement and detection methods for nocturnal animal images

1.
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
2.
Aviation Industry Corporation of China Luoyang Electro-Optical Equipment Research Institute, Luoyang 471023, China

Funds: Supported by National Natural Science Foundation of China (No.62175144); Beijing Engineering Research Center of Aerial Intelligent Remote Sensing Equipments Open Fund (No. AIRSE20233)

More Information

Corresponding author: jbchen@shu.edu.cn

摘要

摘要:
为了解决动物夜间实时监测所面临的图像曝光度低、对比度低、特征提取困难等问题，通过研究轻量化自监督深度神经网络Zero-Denoise和改进型YOLOv8模型，来进行夜间动物目标的图像增强与精准识别。首先，通过轻量化的PDCE-Net进行第一阶段快速增强。提出了一个新的光照损失函数，并利用参数可调的Gamma校正原图与快速增强图，在基于Retinex原理和最大熵理论的PRED-Net中进行第二阶段的重增强。然后，改进YOLOv8模型，并对重增强后的图像进行目标识别。最后，在LOL（Low-Light dataset）数据集与自建动物数据集进行实验分析，验证Zero-Denoise网络和改进型YOLOv8模型对于夜间动物目标监测的改善效果。试验结果显示，Zero-Denoise 网络在 LOL 数据集上的PSNR、SSIM与MAE指标达到28.53、0.76、26.15，结合改进型 YOLOv8 在自建动物数据集上比 YOLOv8 基线模型的mAP值提升7.1%。使用 Zero-Denoise和改进型YOLOv8能获得良好的夜间动物目标图像。结果表明所提方法可用于进一步研究夜间动物目标精确监测。
- 夜间动物监测 /
- 低光增强 /
- 自监督学习 /
- Retinex /
- 低光去噪
Abstract:
In order to solve the problems of low image exposure, low contrast and difficulty of feature extraction in real-time animal monitoring at night, we proposed a lightweight self-supervised deep neural network Zero-Denoise and an improved YOLOv8 model for image enhancement and accurate recognition of nocturnal animal targets. The first stage of rapid enhancement was performed by lightweight PDCE-Net. A new lighting loss function was proposed, and the second stage of re-enhancement was carried out in PRED-Net based on the Retinex principle and the maximum entropy theory, using the original image and fast enhancement image corrected by the parameter adjustable Gamma. Then, the YOLOv8 model was improved to recognize the re-enhanced image. Finally, experimental analysis was conducted on the LOL dataset and the self-built animal dataset to verify the improvement of the Zero-Denoise network and YOLOv8 model for nocturnal animal target monitoring. The experimental results show that the PSNR, SSIM, and MAE indicators of the Zero-Denoise network on the LOL dataset reached 28.53, 0.76, and 26.15, respectively. Combined with the improved YOLOv8, the mAP value of the baseline model on the self-built animal dataset increased by 7.1% compared to YOLOv8. Zero-Denoise and improved YOLOv8 can achieve good quality images of nocturnal animal targets, which can be helpful in further study of accurate methods of monitoring these targets.
- nocturnal animal detection /
- low-light enhancement /
- self-supervised learning /
- Retinex /
- low-light denoising

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图 1 部分卷积PConv原理图

Figure 1. Convolution principle of PConv

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图 2 Zero-Denoise网络整体结构

Figure 2. Overall structure of Zero-Denoise network

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图 3 SA注意力机制原理图

Figure 3. Schematic diagram of Shuffle Attention mechanism

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图 4 RepGFPN整体结构

Figure 4. Overall structure of RepGPFN

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图 5 改进YOLOv8网络整体结构

Figure 5. Overall structure of improved YOLOv8

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图 6 数据集各类动物种类

Figure 6. Various animal species in the dataset

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图 7 各类先进图像增强算法对比

Figure 7. Performance comparison of various advanced image enhancement algorithms

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图 8 增强与未增强的目标检测可视化结果

Figure 8. Visualization results of enhanced and unenhanced target detection

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表 1 不同算法评价结果

Table 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

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表 2 改进YOLOv8消融实验结果

Table 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%)

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表 3 不同算法对改进YOLOv8的效果

Table 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%)

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表 4 增强与未增强的目标检测可视化结果对比

Table 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	未增强图像出现漏检数量1，阴影处鸽类未检出
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	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
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	未增强图像出现漏检数量1，深处的鸭类未检出
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	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
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	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
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	增强图像中兔类检测精度提升明显，从0.77提升至0.90

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