ArticleSensors (Basel, Switzerland)2024
Lightweight Vehicle Detection Based on Improved YOLOv5s.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Rice Disease Detection: TLI-YOLO Innovative Approach for Enhanced Detection and Mobile Compatibility.Sensors (Basel, Switzerland) · 2025Article
- Article
- YOLO-BOS: An Emerging Approach for Vehicle Detection with a Novel BRSA Mechanism.Sensors (Basel, Switzerland) · 2024Article
- A Lightweight Vehicle Detection Method Fusing GSConv and Coordinate Attention Mechanism.Sensors (Basel, Switzerland) · 2024Article
- Improved YOLOv5 s and transfer learning for floater detection.Science progressArticle
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Authors and funding
8 authors.
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Abstract
A vehicle detection algorithm is an indispensable component of intelligent traffic management and control systems, influencing the efficiency and functionality of the system. In this paper, we propose a lightweight improvement method for the YOLOv5 algorithm based on integrated perceptual attention, with few parameters and high detection accuracy. First, we propose a lightweight module IPA with a Transformer encoder based on integrated perceptual attention, which leads to a reduction in the number of parameters while capturing global dependencies for richer contextual information. Second, we propose a lightweight and efficient multiscale spatial channel reconstruction (MSCCR) module that does not increase parameter and computational complexity and facilitates representative feature learning. Finally, we incorporate the IPA module and the MSCCR module into the YOLOv5s backbone network to reduce model parameters and improve accuracy. The test results show that, compared with the original model, the model parameters decrease by about 9%, the average accuracy (mAP@50) increases by 3.1%, and the FLOPS does not increase.
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