LCA-YOLOv8: lightweight object detection with CCFF and ACMix for safety helmets

In practical production processes, wearing a safety helmet is a crucial guarantee of safe production. Helmet detection can effectively reduce the probability of safety accidents. However, existing algorithms often suffer from large parameter quantities, high computational complexity, and poor real-time performance. To address these issues, we propose a lightweight helmet-wearing detection algorithm based on the YOLO v8 framework, named LCA-YOLOv8. We adopt a lightweight CCFF module to reduce the parameters of the neck layer and use the ACMix module to increase network depth and enhance the neural network's ability to capture both shallow and deep semantic information, thereby improving feature representation capability. Experimental results show that on the public SHWD dataset, compared with YOLO v8 baseline, our algorithm not only reduces the parameter count by 30.4% and the floating-point operations by 13.6%, but also achieves a precision of 92.7%, effectively balancing high performance and real-time requirements to meet the needs of actual safe production.