Lightweight object detection model for food freezer warehouses

Warehouses are critical logistics nodes, with food freezer warehouses playing a key role in ensuring food quality while facing challenges such as high-density item distribution and extremely low temperatures required for occupational safety. Traditional management methods struggle to meet these demands, underscoring the need for intelligent and digital solutions to improve efficiency and mitigate safety risks. This study proposes the YOLOv8-RSS model, a lightweight and high-precision approach tailored for food freezer warehouse scenarios. The model incorporates the novel C2f_RDB module, which enhances detection accuracy while reducing parameter count and computational load. Additionally, the SimAM attention mechanism is applied to the Backbone's final layer, enabling focus on critical image information without increasing parameters. Soft-NMS replaces the traditional NMS method, further improving detection accuracy. Experiments conducted on the food freezer warehouse dataset demonstrate that the YOLOv8-RSS model reduced the parameter count by 0.05 M, decreased FLOPs by 0.8G, increased [email protected] by 1.4%, and improved [email protected]:0.95 by 3.9%. The YOLOv8-RSS is designed to meet the complex detection demands in food freezer warehouses, enabling precise and rapid detection of personnel and forklifts. It provides strong technical support for addressing various challenges in these environments and holds significant application value.