基于改进YOLOv4颈部优化网络的安全帽佩戴检测方法

针对安全帽佩戴检测时易受复杂背景干扰，解决YOLOv4网络检测速度慢、内存消耗大、计算复杂度高、对硬件性能要求较高等问题，引入改进YOLOv4算法优化安全帽佩戴检测方法。引入MobileNet网络轻量化YOLOv4、跨越模块特征融合，实现高层语义特征和低层语义特征有效融合。针对图像中小目标分辨率低，信息特征少，多尺度并存，导致在连续卷积过程中易丢失特征信息等问题，采用改进特征金字塔FPN和注意力机制等颈部优化策略聚焦目标信息，弱化安全帽检测时背景信息的干扰。仿真结果表明，基于改进的YOLOv4颈部优化网络安全帽佩戴检测算法在CPU平台下的检测速度为34.28 FPS，是基础YOLOv4网络的16倍，检测精度提升了4.21%，检测速度与检测精度达到平衡。

Helmet wearing detection method based on improved YOLOv4 neck optimized network

Helmet wearing detection is susceptible to interference from complex background. To address this challenge, the YOLOv4 algorithm is employed. However, YOLOv4 faces issues such as slow detection speed, high memory consumption, computational complexity and demanding hardware performance requirements. This study integrates the MobileNet network to alleviate these challenges in YOLOv4. Additionally, cross-module feature fusion is introduced in MobileNet network, enabling effective fusion of high-level semantic features and low-level semantic features. Despite these advancements, small targets in images poses problems such as low resolution, limited informative features, coexistence of multiple scales, and potenial loss of feature information during continuous convolution. To mitigate these issues, this paper introduces neck optimization strategies, such as improving the feature pyramid FPN and introducing/ improving attention mechanism. These strategies focus on target information and reduce interference from background information during helmet detection. Simulation results show that the improved YOLOv4 neck-optimized network achieves a detection speed of 34.28FPS on the CPU platform, which is about 16 times faster than YOLOv4 network. Moreover, its detection accuracy is 4.21% higher than that of the YOLOv4 algorithm. This optimized algorithm strikes a balance between speed and accuracy.