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-separable neck network with attention, which enhances the detection accuracy and speed of targets in complex environments. 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 embedded 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.