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Helmet recognition algorithms based on deep learning aim to enable unmanned full-time detection and record violations such as failure to wear a helmet. However, in actual scenarios, weather and human factors can be complicated, which poses challenges for safety helmet detection. Camera shaking and head occlusion are common issues that can lead to inaccurate results and low availability. To address these practical problems, this paper proposes a novel helmet detection algorithm called DAAM-YOLOv5. The DAAM-YOLOv5 algorithm enriches the diversity of datasets under different weather conditions to improve the mAP of the model in corresponding scenarios by using Mosaic-9 data enhancement. Additionally, this paper introduces a novel dynamic anchor box mechanism, K-DAFS, into this algorithm and enhances the generation speed of the blocked target anchor boxes by using bidirectional feature fusion (BFF). Furthermore, by using an attention mechanism, this paper redistributes the weight of objects in a picture and appropriately reduces the model’s sensitivity to the edge information of occluded objects through pooling. This approach improves the model’s generalization ability, which aligns with practical application requirements. To evaluate the proposed algorithm, this paper adopts the region of interest (ROI) detection strategy and carries out experiments on specific, real datasets. Compared with traditional deep learning algorithms on the same datasets, our method effectively distinguishes helmet-wearing conditions even when head information is occluded and improves the detection speed of the model. Moreover, compared with the YOLOv5s algorithm, the proposed algorithm increases the mAP and FPS by 4.32% and 9 frames/s, respectively.

Delay in feedback is inevitable in a multi-agent system due to time lags in information processing for self-organization. The well-known Cucker-Smale model incorporated with this information processing delay has been recently studied, and it was shown (at least for a two-agent system) that as long as the delay is below a threshold value, the system exhibits the flocking behavior where the agents ultimately reach the same velocity. Numerical studies however suggest that the threshold value established for the delay is not optimal, and the Cucke-Smale system remains to have the flocking property for large delay. Here, we rigorously prove that flocking behavior emerges for a large information processing delay, and we establish the relationship between the admissible size of time delay and the system’s parameters and initial data.