The lightweight YOLO-WWSP model is designed to address two main challenges in the detection model for workwear wearing—large parameter count and insufficient detection accuracy. First, a lightweight detection head based on shared convolution and task alignment is developed, which reduces parameters while aligning localization and classification tasks. Second, a depthwise separable convolution and element-wise multiplication operations at the neck of the model is used, which reduces the parameter and computational complexity of the model. Finally, a grouping coordinate attention mechanism that integrates multiscale local information is designed to enhance the feature extraction capability of the backbone, and parameter pruning techniques is used to reduce the number of redundant parameters in the model. The experimental results show that compared with the baseline model YOLOv8n, YOLO-WWSP exhibits a 69.8% decrease in parameter count and a 39.1% decrease in computational complexity, and mAP@0.5 and mAP@0.5∶0.95 increased by 0.7 percentage points and 1.5 percentage points, respectively, demonstrating the effectiveness of YOLO-WWSP in detecting improper workwear wearing.