Camouflage detection: Optimization-based computer vision for Alligator sinensis with low detectability in complex wild environments

Abstract

Alligator sinensis is an extremely rare species that possesses excellent camouflage, allowing it to fit perfectly into its natural environment. The use of camouflage makes detection difficult for both humans and automated systems, highlighting the importance of modern technologies for animal monitoring. To address this issue, we present YOLO v8-SIM, an innovative detection technique specifically developed to significantly enhance the identification precision. YOLO v8-SIM utilizes a sophisticated dual-layer attention mechanism, an optimized loss function called inner intersection-over-union (IoU), and a technique called slim-neck cross-layer hopping. The results of our study demonstrate that the model achieves an accuracy rate of 91 %, a recall rate of 89.9 %, and a mean average precision (mAP) of 92.3 % and an IoU threshold of 0.5. In addition, the model operates at a frame rate of 72.21 frames per second (FPS) and excels at accurately recognizing objects that are partially visible or smaller in size. To further improve our initiatives, we suggest creating an open-source collection of data that showcases A. sinensis in its native environment while using camouflage techniques. These developments collectively enhance the ability to detect disguised animals, thereby promoting the monitoring and protection of biodiversity, and supporting ecosystem sustainability. • Uses ResNet-18 with Biformer and Reverse Attention to enhance detection in camouflaged settings by focusing network attention effectively. • The Inner-IoU loss enhances scale generalization in detection by optimizing bounding box regression, while Slim Neck prunes the network to reduce parameters without losing accuracy. • The Slim Neck mechanism prunes and compresses the network without losing detection accuracy by reducing parameters strategically. • Code and datasets are publicly available

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