To address the current challenges in detecting weld porosity during vehicle production, such as difficulty in detection, low efficiency, and low accuracy, an improved model based on YOLOv5s is proposed. The model incorporates both spatial and channel attention modules in the Backbone network to enhance the feature extraction capability for weld porosity. Additionally, a bidirectional feature pyramid module is introduced in the Neck network to improve the feature fusion capability for small targets. Then, the loss function of the model is adjusted to strengthen the generalization ability for detecting small targets, specifically improving the localization accuracy of small defects. The improved model is validated on a vehicle production weld dataset, results show that the proposed model achieved a mean average precision of 62.9% and a detection frame rate of 89.72 frame/s. Compared with current mainstream object detection algorithms, the proposed model demonstrates superior overall performance.