Autonomous driving target detection based on improved YOLOv7

Bowen Zheng; Huacai Lu; Ming Li

doi:10.1117/12.3031141

19 July 2024 Autonomous driving target detection based on improved YOLOv7

Bowen Zheng, Huacai Lu, Ming Li

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131811U (2024) https://doi.org/10.1117/12.3031141
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Aiming at the problems of false detection and missing detection caused by dense vehicle targets, mutual occlusion, and too small targets in automatic driving scenarios, an improved YOLOv7 target detection algorithm is proposed. In the detection task, the SENet module is introduced to learn the feature weights according to the loss function, which can make the effective feature maps have more weight, and the invalid or ineffective feature maps have less weight, so that the training model can achieve better results. The normalization-based NAM attention module is introduced, which reduces the weights of less significant features, making them computationally more efficient while maintaining the same performance. To verify the validity of the experimental results, the experimental results of the improved YOLOv7 were compared with the experimental results of YOLOv5 and the original YOLOv7 model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bowen Zheng, Huacai Lu, and Ming Li "Autonomous driving target detection based on improved YOLOv7", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131811U (19 July 2024); https://doi.org/10.1117/12.3031141

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