ArticlePeerJ. Computer science2023
Vehicle target detection method based on improved YOLO V3 network model.
Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Passable: An Intelligent Traffic Light System with Integrated Incident Detection and Vehicle Alerting.Sensors (Basel, Switzerland) · 2025Article
- YOLO-BOS: An Emerging Approach for Vehicle Detection with a Novel BRSA Mechanism.Sensors (Basel, Switzerland) · 2024Article
- A Multi-Scale-Enhanced YOLO-V5 Model for Detecting Small Objects in Remote Sensing Image Information.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors.
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Abstract
For the problem of insufficient small target detection ability of the existing network model, a vehicle target detection method based on the improved YOLO V3 network model is proposed in the article. The improvement of the algorithm model can effectively improve the detection ability of small target vehicles in aerial photography. The optimization and adjustment of the anchor box and the improvement of the network residual module have improved the small target detection effect of the algorithm. Furthermore, the introduction of the rectangular prediction frame with orientation angles into the model of this article can improve the vehicle positioning efficiency of the algorithm, greatly reduce the problem of wrong detection and missed detection of vehicles in the model, and provide ideas for solving related problems. Experiments show that the accuracy rate of the improved algorithm model is 89.3%. Compared to the YOLO V3 algorithm, it is improved by 15.9%. The recall rate is improved by 16%, and the F1 value is also improved by 15.9%, which greatly increased the detection efficiency of aerial vehicles.
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