Abstract
Small objects often suffer from size and resolution limitations, resulting in poor detection performance when employing traditional object detection models. To address these challenges, we propose SDGC-YOLOv5, a novel model based on YOLOv5. Our contributions are as follows: a) Replacement of the original convolution layer with spatial depth convolution (SD) to enable dense feature extraction and improve the detection of small objects. b) Utilization of a lightweight global context network (GC) to enhance small object recognition capabilities. c) Incorporation of the shifted window scheme to enhance the extraction of small target features at the bottom of the pyramid in the YOLOv5 architecture. Experimental results demonstrate the superiority of SDGC-YOLOv5 over existing YOLO algorithms on multiple publicly available datasets, including MS-COCO, Pascal-VOC2012, and DOTA, achieving performance improvements of 3%, 4.4%, and 0.6%, respectively. Moreover, an unprecedented mAP accuracy of 91% is achieved on the self-built dataset PhotovoltaicPanels. In summary, SDGC-YOLOv5 effectively addresses the limitations of traditional YOLO-based object detection methods and exhibits remarkable performance in detecting small objects with low resolution and small size.
Y. Chen—These authors contributed equally to this work.
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Acknowledgement
This work was supported by the Inner Mongolia Natural Science Foundation of China under Grant No. 2021MS06016 and No. 2023MS06020. This work was supported in part by the Inner Mongolia Science and Technology Plan Project (No. 2020GG0187).
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Lu, Z., Chen, Y., Li, S., Ma, M. (2023). SDGC-YOLOv5: A More Accurate Model for Small Object Detection. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14260. Springer, Cham. https://doi.org/10.1007/978-3-031-44195-0_17
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