An improved safety helmet detection algorithm based on YOLOv8 in construction site

Construction site safety has become increasingly important with the rapid development of industrialisation around the world. To enhance the safety management of construction sites and protect the lives of workers, information technology tools such as video surveillance and image detection can be utilised. However, images in complex environments such as haze and dusty site scenes may be distorted, affecting the accuracy of helmet wear detection. In order to solve these problems, this paper designs and implements In this paper, we design an improved algorithm for YOLOv8 target detection model that incorporates the AOD-Net dehaze model. Firstly, the AOD-Net defogging model is used to pre-process the captured images for defogging, and by adding the ECA attention mechanism to the network, it helps the network to better capture and utilize the information of the image details during the training; Adaptive Histogram Equalization (AHE) is added to post-process the images, which effectively improves the quantitative indexes of PSNR and SSIM, and generates the dataset for training. Second, the improved YOLOv8 is used for helmet target detection, which improves the small-scale feature extraction capability by introducing deformable convolutional DCNv2; adds the AUX-head module in YOLOv7 to provide richer gradient information for the model to help the training; replaces the original up-sampling of the model with the lightweight up-sampling operator CARAFE, which increases the sensing field and reduces the model computing cost; replace the YOLOv8 loss function with SIoU to achieve faster convergence in the training phase; experimental results show that: the accuracy of the improved YOLOv8 network model reaches 0.907, compared to the original YOLOv8 model, the accuracy of the network model is improved by 3.047%, which provides higher detection accuracy and reasoning speed, and meets the requirements for the safety helmet wearing detection task under the dusty environment, without increasing the number of parameters and the cost of detection. This provides higher detection accuracy and inference speed without increasing the number of parameters and the cost, which meets the needs of helmet wear detection in dusty and foggy environment.