With the increasing maturity of smart grid and computer vision technologies, the use of mobile edge devices in collaboration with “cloud” to monitor the safety condition of construction workers wearing helmets has been widely used in power system construction scenarios. In this paper, based on YOLOv5, a new target detector-L-YOLOv5 is proposed to improve the accuracy and efficiency of the detector by optimizing key components. First, the traditional convolutional layer is replaced by using iconv in the backbone network; second, based on ShuffleNetV2, the MS attention mechanism module is used to improve the network structure, and a new backbone network structure isnet is proposed to reduce the computation and improve the accuracy of the detector while ensuring that the feature fusion performance is not affected. In the neck, we propose a GSPAN structure, firstly, we use 1*1 odconv to make the number of channels of features consistent with the minimum number of channels of backbone network output, which can effectively enhance the feature extraction ability of the network and reduce the network parameters; in addition, we down sample the GSPAN again and add a new feature scale to help the detector detect more targets and solve the problem of false detection due to occlusion and overlap caused by false detection, missed detection, and insufficient feature extraction ability. The experimental results show that the L-YOLOv5 model on RTX3090 is 44.3FPS, which is 82% less than the parameter amount of YOLOv5 and 3.3% more accurate on average, achieving excellent performance and satisfying the real-time detection of construction workers wearing helmets in construction scenarios of power systems.