A lightweight giant panda object detection model with attention mechanism
To address the problem of poor detection performance of giant pandas in complex environments and the low efficiency of object detection models on resource-limited embedded devices,a lightweight giant panda detection model called GP-YOLOv5n is proposed.The model is based on YOLOv5n and improved by introducing a depth-sepa-rable neck network with attention,which enhances the detection accuracy and speed of targets in complex environ-ments.Moreover,the model adopts Alpha-IoU in the bounding box regression loss function to improve the bounding box localization accuracy of targets.After training on a homemade giant panda dataset,the model is optimized for em-bedded devices and deployed on Jetson Nano.Experimental results show that the improved model achieves 97.8％and 73.6％in mAP50 and mAP50:95 metrics respectively,which are 2.7％and 9.2％higher than the original model.The detection speed of the model on embedded devices reaches 15.12 f/s,which can accurately and real-time detect the giant pandas in complex environments.
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