In order to effectively address the issue of brown bear intrusions in the Qinghai region, we are developing a rapid and accurate response early warning system. In consideration of the cost of promotion, we have adopted a method where the front-end devices collect data and transmit it back to the server for information processing. Since the computational resources of front-end devices are typically limited, This paper decides to optimize and improve the You Only Look Once (YOLO) series of algorithms. Replacing the traditional convolution with AKconv (Alterable Kernel Convolution) as the main structure, Introducing the BiFormer (Bilateral Transformer) attention mechanism in the convolutional part of Detect, to enhance the ability of target feature extraction. And in order to further enhance the effect of feature extraction, concatenating the SAM(Segment Anything Model) before the YOLOV8,to assist YOLOV8 in feature extraction. The improved YOLOV8, compared to the original YOLOV8, precision increased by 1.7.
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