Abstract: To address the issues of low efficiency and poor real-time performance in the management of military warehouse equipment,as well as the shortcomings of existing detection models in complex warehouse scenarios－such as missed detection of small targets,false detection of dense targets,poor environmental robustness,and anchor mismatch－this paper proposes an improved YOLOv5-based detection method for warehouse equipment.Based on the YOLOv5s model,the proposed approach incorporates the following improvements:constructing a multi-scale feature enhancement network to improve the recognition capability for small targets;adopting DIoU-NMS to enhance the accuracy of dense target detection;introducing the CBAM attention mechanism and a frequency-domain illumination suppression module to strengthen the model's adaptability to complex environments;and optimizing anchor matching through K-means++ re-clustering.Experimental results show that the improved model maintains lightweight characteristics while significantly increasing average precision,with both precision and recall outperforming the original model.This method can effectively support intelligent inventory and management of warehouse equipment.

Key words: Armory equipment management, Object detection, YOLOv5, Intelligent inventory