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

: 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 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.