Lightweight Detection Methods for Multi-Scale Targets in Complex Scenarios

Detecting multi-scale objects in complex scenes is crucial for real-time applications in building safety, where accurate monitoring of safety equipment under challenging conditions is essential. In this paper, we propose LAP-YOLO (Lightweight Aggregate Perception based on "You Only Look Once"), which integrates a Feature-Aware Aggregation (FAA) module to enhance feature representation and a Lightweight Information Diffusion (LID) detection head to improve small-object detection efficiency with minimal computational overhead. Experimental results demonstrate that LAP-YOLO surpasses other real-time models on the safety equipment dataset, achieving a 39% reduction in parameters and a 57% decrease in FLOPs compared to YOLOv8, while maintaining detection accuracy under the same conditions. This lightweight and effective approach presents a promising solution for enhancing construction site safety.