3L-YOLO: A Lightweight Low-Light Object Detection Algorithm

• Published in: Applied sciences Vol. 15; no. 1; p. 90
• Main Authors: Han, Zhenqi, Yue, Zhen, Liu, Lizhuang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2025
• Subjects: Accuracy; Agricultural production; Algorithms; atrous convolution; attention mechanism; Deep learning; Design; Efficiency; low-light object detection; Sensors; Telematics; YOLO
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15010090

Object detection in low-light conditions presents significant challenges due to issues such as weak contrast, high noise, and blurred boundaries. Existing methods often use image enhancement to improve detection, which results in a large amount of computational resource consumption. To address these challenges, this paper proposes a detection method, 3L-YOLO, based on YOLOv8n, which eliminates the need for image enhancement modules. First, we introduce switchable atrous convolution (SAConv) into the C2f module of YOLOv8n, improving the model’s ability to efficiently capture global contextual information. Second, we present a multi-scale neck module that aggregates shallow features and incorporates a channel attention mechanism to prioritize the most relevant features. Third, we introduce a dynamic detection head, which employs a cascade of spatial, scale, and channel attention mechanisms to enhance detection accuracy and robustness. Finally, we replace the original loss function with MPDIoU loss, improving bounding box regression and overall reliability. Additionally, we create a synthetic low-light dataset to evaluate the performance of the proposed method. Extensive experiments on the ExDark, ExDark+, and DARK FACE datasets demonstrate that 3L-YOLO outperforms YOLOv8n in low-light object detection, with improvements in mAP@0.5 of 2.7%, 4.3%, and 1.4%, respectively, across the three datasets. In comparison to the LOL-YOLO low-light object detection algorithm, 3L-YOLO requires 16.9 GFLOPs, representing a reduction of 4 GFLOPs.