FEFD-YOLOV5: A Helmet Detection Algorithm Combined with Feature Enhancement and Feature Denoising
Yiduo Zhang, Yi Qiu, Huihui Bai
Abstract
In intelligent surveillance of construction sites, safety helmet detection is of great significance. However, due to the small size of safety helmets and the presence of high levels of noise in construction scenarios, existing detection methods often encounter issues related to insufficient accuracy and robustness. To address this challenge, this paper introduces a new safety helmet detection algorithm, FEFD-YOLOV5. The FEFD-YOLOV5 algorithm enhances detection performance by adding a shallow detection head specifically for small target detection and incorporating an SENet channel attention module to compress global spatial information, thus improving the model’s mean average precision (mAP) in corresponding scenarios. Additionally, a novel denoise module is introduced in this algorithm, ensuring the model maintains high accuracy and robustness under various noise conditions, thereby enhancing the model’s generalization capability to meet real-world scenario demands. Experimental results show that the proposed improved algorithm achieves a detection accuracy of 94.89% in noiseless environments, and still reaches 91.55% in high-noise environments, demonstrating superior detection efficacy compared to the original algorithm.