Wang, Z., Men, S., Bai, Y., Yuan, Y., Wang, J., Wang, K., & Zhang, L. (2024). Improved Small Object Detection Algorithm CRL-YOLOv5. Preprints. https://doi.org/10.20944/preprints202408.1218.v1
Chicago/Turabian Style
Wang, Z., Kanglei Wang and Lei Zhang. 2024 "Improved Small Object Detection Algorithm CRL-YOLOv5" Preprints. https://doi.org/10.20944/preprints202408.1218.v1
Abstract
Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the CBAM attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images.
Keywords
Small Object Detection; Attention Mechanisms; Contextual Information; YOLOv5
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
