Logistics warehouse detection based on deep learning

With the rapid development of e-commerce, the modern logistics industry has put forward higher requirements for the detection efficiency and accuracy of express packages. To address this challenge, this paper proposes a deep learningbased package automatic detection system for express delivery. The system adopts the improved YOLOv8 algorithm and integrates the Global Attention Module (GAM), Bidirectional Feature Pyramid Network (BiFPN) and Concat2 modules. It is designed to improve the accuracy and real-time of express package detection. Through in-depth training with 300 Epochs, our model has demonstrated superior performance in key performance indicators such as accuracy, recall, mAP50, and F1 scores, significantly exceeding existing technologies. The experimental results show that the model can effectively identify the irregular shape and variable size of the logistics package, which provides an efficient and reliable solution for logistics automation.