夜间动物图像自监督学习增强与检测方法OA北大核心CSTPCD

In order to solve the problems of low image exposure,low contrast and difficulty of feature extrac-tion 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 nocturn-al 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 enhance-ment image corrected by the parameter adjustable Gamma.Then,the YOLOv8 model was improved to re-cognize 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 indicat-ors of the Zero-Denoise network on the LOL dataset reach 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 increases 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.