Abstract
AbstractMonitoring the critically endangered Amur tiger (Panthera tigris altaica) is crucial for the conservation of the natural environment. This paper proposes a novel lightweight deep‐learning network for detecting Amur tigers in mobile and resource‐constrained environments, like those using unmanned aerial vehicles. The network uses an anchor‐free mechanism with modified CSPNet and cross stage partial (CSP)‐path aggregation network (PAN) structures, which improves the model's feature extraction capabilities. Label assignment strategy is also improved to stabilize the model training process. Additionally, the random Mosaic and Mixup data augmentation strategy is utilized to address the overfitting issue due to insufficient data in the data set. The model achieves 55.5% mean average precision (mAP [0.5:0.95]) with only 0.617 million parameters and 73.58 frames per second on mobile CPU with pixels input. Results show that the model is an accurate, fast, and practical detector of the Amur tiger, serving as a reference for wildlife detectors of other species.
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