Optimization and implementation of vehicle detection algorithm for tiny objects

Jing Shi

doi:10.1117/12.3004036

11 October 2023 Optimization and implementation of vehicle detection algorithm for tiny objects

Jing Shi

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128000Q (2023) https://doi.org/10.1117/12.3004036
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

To address the issue of current target detection models struggling to detect small vehicles and complex depth models being ill-suited for embedded platforms with limited computing power and memory, NWD and IoU are combined for tiny object detection, and the lightweight backbone network EfficientFormer is used to replace the YOLOv5 model backbone, which finally improves the tiny object detection effect while reducing the model parameters to achieve the lightweight depth model. The experimental results show that on the actual data set collected from the project, the mAP reaches 82.1%, and the number of parameters is reduced by 8.5MB.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jing Shi "Optimization and implementation of vehicle detection algorithm for tiny objects", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128000Q (11 October 2023); https://doi.org/10.1117/12.3004036

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