基于多级特征提取的低光照目标检测算法

doi:10.3778/j.issn.1002-8331.2308-0093

摘要/Abstract

摘要： 自然环境下普遍存在着低光照场景，导致目标检测任务精度降低。提出了一种低光照目标检测算法MLFE-YOLOX。引入轻量级图像增强算法IAT，还原低照度图像更多的细节。设计了一种多级特征提取的CSP-M模块，加强模型在低光照条件下图像的特征提取能力。引入卷积注意力机制CBAM，自适应测量目标位置与背景信息的相关程度，降低背景信息带来的干扰。设计了多级特征融合模块CSP-MC，增强模型融合多级特征和发掘并融合静态和动态上下文信息的能力。采用ExDark、UFDD数据集进行实验验证，实验结果表明，该方法有效克服了光照不足带来的影响，与主流算法相比检测精度明显提升。

关键词: 低光照, 多级特征, YOLOX, 注意力机制

Abstract: The low-light scene is prevalent in natural environments which can reduce target detection accuracy. A low-light object detection algorithm MLFE-YOLOX is proposed. Firstly, the lightweight image enhancement algorithm IAT is introduced to restore more details of low-illumination images. Secondly, a CSP-M module for multi-level feature extraction is designed to strengthen the performance of the feature extraction model under low light conditions. Then, the convolutional attention mechanism CBAM is introduced to adaptively measure the correlation between target position and background information, which reduces the interference caused by background information. Finally, a multi-level feature fusion module CSP-MC is designed to enhance the model’s ability to fuse multi-level features and the ability to explore and fuse static and dynamic contextual information. ExDark and UFDD datasets are used for experimental verification, and the experimental results show that the proposed method effectively overcomes the influence caused by under illumination, and the detection accuracy is significantly improved in comparison with the mainstream algorithms.

Key words: low light, multi-level features, YOLOX, attention mechanism

谭豪, 张惊雷, 贾鑫. 基于多级特征提取的低光照目标检测算法[J]. 计算机工程与应用, 2024, 60(24): 235-242.

TAN hao, ZHANG Jinglei, JIA Xin. Low-Light Target Detection Algorithm Based on Multi-Level Feature Extraction[J]. Computer Engineering and Applications, 2024, 60(24): 235-242.

参考文献

[1] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
[2] WANG K, LIU M Z. Object recognition at night scene based on DCGAN and faster R-CNN[J]. IEEE Access, 2020, 8: 193168-193182.
[3] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision (ECCV 2016), Amsterdam, The Netherlands, October 11-14, 2016: 21-37.
[4] TAN M, PANG R, LE Q V. EfficientDet: scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 10781-10790.
[5] 王琳毅, 白静, 李文静, 等. YOLO系列目标检测算法研究进展[J]. 计算机工程与应用, 2023, 59(14): 15-29.
WANG L Y, BAI J, LI W J, et al. Research progress of YOLO series target detection algorithms[J]. Computer Engineering and Applications, 2023, 59(14):15-29.
[6] MA R, BAO K, YIN Y. Improved ship object detection in low-illumination environments using RetinaMFANet[J]. Journal of Marine Science and Engineering, 2022, 10(12): 1996.
[7] WANG J, YANG P, LIU Y, et al. Research on improved YOLOv5 for low-light environment object detection[J]. Electronics, 2023, 12(14): 3089.
[8] CHEN Y, SUN X, XU L, et al. Application of YOLOv4 algorithm for foreign object detection on a belt conveyor in a low-illumination environment[J]. Sensors, 2022, 22(18): 6851.
[9] CUI Z, LI K, GU L, et al. You only need 90k parameters to adapt light: a light weight tran-sformer for image enhancement and exposure correction[C]//Proceedings of the 33rd British Machine Vision Conference 2022 (BMVC 2022), London, UK, November 21-24, 2022.
[10] WEI C, WANG W, YANG W, et al. Deep retinex decomposition for low-light enhancement[J]. arXiv:1808.04560, 2018.
[11] ZHU X, SU W, LU L, et al. Deformable DETR: deformable transformers for end-to-end object detection[J]. arXiv:2010.
04159, 2020.
[12] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.
[13] GE Z, LIU S, WANG F, et al. YOLOx: exceeding YOLO series in 2021[J]. arXiv:2107.08430, 2021.
[14] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J]. arXiv:2207.02696, 2022.
[15] LI Y, YAO T, PAN Y, et al. Contextual transformer networks for visual recognition[J]. arXiv:2107.12292, 2021.
[16] SASAGAWA Y, NAGAHARA H. YOLO in the dark-domain adaptation method for merging multiple models[C]//Proceedings of the 16th European Conference on Computer Vision (ECCV 2020), Glasgow, UK, August 23-28, 2020: 345-359.
[17] NADA H, SINDAGI V A, ZHANG H, et al. Pushing the limits of unconstrained face detection: a challenge dataset and baseline results[C]//Proceedings of the 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2018: 1-10.
[18] SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 618-626.