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Lightweight seatbelt detection algorithm for mobile device
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AI
DOI：10.1007/s11042-023-14555-2.png)
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Abstract
En 中文
It is an important task of modern traffic management for traffic police to inspect whether drivers wear seatbelts, and mobile law enforcement brings convenience to traffic police. However, due to the efficiency and memory constraints of mobile devices, the intelligent algorithm with significant parameters cannot be directly used on mobile devices. So, it is a challenge to detect driver and seatbelt by object detection algorithm on mobile. In order to solve this problem, we propose an efficient and lightweight model for seatbelt detection. First, we visualize the layers of SSD MobileNet V2 and delete the feature channels with low contribution and high similarity. Then we can get the pruned SlimSSDMV2 model for driver and seatbelt detection. Second, the LSD liner segment detection multipoint fitting algorithm is used for screening the undetected seatbelt area, which can further improve the detection performance of the model. We compare our model with other existing methods, and the experimental results demonstrate that our model performs better in practice.
Keywords:
Seatbelt detection
Channel pruning
SlimSSDMV2
LSD linear detection
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W