After learning, the object-detection algorithm can automatically detect whether the riders of electric mopeds are wearing helmets, thereby saving regulatory labor costs. However, the complex environmental background and headwear similar to helmets can easily cause a large number of false negatives and false positives, increasing the difficulty of detection. This paper proposes the YOLOv8n-Improved object-detection algorithm. First, in the neck part, the algorithm uses a simplified weighted bi-directional feature pyramid network structure to remove single input nodes, add connection edges, and attach path weights according to the importance of features. This structure optimizes the algorithm’s multiscale feature-fusion capability while improving computational efficiency. In the head part, the algorithm uses the scale-sensitive intersection over union loss function to introduce the vector angle between the predicted and ground-truth boxes, redefining the penalty metric. This improvement speeds up the convergence process of the network and improves the accuracy of the model. After comparative validation on the test set, the YOLOv8n-Improved algorithm shows a 1.37% and 3.16% increase in the average precision (AP) metric for electric moped and helmet detection, respectively, and a 2.27% increase in the overall mean AP metric, with a reduction in both false negatives and false positives for the two categories.

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