FFE-YOLOv8：交通场景遮挡目标检测算法

doi:10.3778/j.issn.1002-8331.2411-0268

摘要/Abstract

摘要： 交通场景下的目标因为密集或遮挡等情况会导致检测困难，针对该问题，提出了一种交通场景目标检测算法FFE-YOLOv8。设计跨层级特征融合模块（cross-level feature fusion module，CFFM），结合浅层细节和深层整体特征，得到更丰富的特征信息指导遮挡目标的检测；设计了双路特征增强模块（dual-channel feature enhancement，DCFE），经两通道同时提取浅层特征的中心区域关键信息以及上下文信息，补充特征的同时避免浅层特征层感受野较小的问题；经自适应特征融合模块（adaptive feature fusion module，AFFM），计算经双路特征补充模块处理后的特征和浅层检测通道特征的相似度从而进行有针对性的增强。在KITTI，CBCL和Udacity数据集上进行实验，结果显示相比于基准网络改进后的算法在精度上分别提升了1.52、1.61、2.47个百分点，改善了遮挡目标的检测效果，证明了该方法的有效性。

关键词: 交通场景, 遮挡目标, YOLOv8, 特征融合, 特征增强

Abstract: Objects in traffic scenes are difficult to detect because of density or occlusion. To solve this problem, a traffic scenes object detection algorithm FFE-YOLOv8 is proposed. Firstly, a cross-level feature fusion module(CFFM) is designed. It combines shallow details and deep features to obtain richer feature information for guiding occlusion detection. Secondly,a dual-channel feature enhancement module(DCFE) is designed. It simultaneously extracts the key information of the central region and the context information of the shallow feature through two channels. This avoids the problem of the small receptive field of the shallow feature layer while supplementing the feature. Finally, an adaptive feature fusion module(AFFM) is used to calculate the similarity between the features processed by the dual-channel feature enhancement module and those of the shallow detection channel. Experiments on the KITTI, CBCL and Udacity datasets show that compared with the benchmark network, the improved algorithm accuracy is increased by 1.52, 1.61 and 2.47 percentage points respectively. The occlusions detection effect is improved, which proves the effectiveness of this method.

Key words: traffic scenes, occlusion object, YOLOv8, feature fusion, feature enhancement

王坤, 冯康威. FFE-YOLOv8：交通场景遮挡目标检测算法[J]. 计算机工程与应用, 2025, 61(15): 156-166.

WANG Kun, FENG Kangwei. FFE-YOLOv8: Occlusion Object Detection Algorithm in Traffic Scenes[J]. Computer Engineering and Applications, 2025, 61(15): 156-166.

参考文献

[1] DENG T M, ZHANG X Y, CHENG X X. A new vehicle dete-ction framework based on feature-guided in the road scene[J]. Computers, Materials & Continua, 2024, 78(1): 533-549.
[2] ZHENG Q H, TIAN X Y, YU Z G, et al. MobileRaT: a lightweight radio transformer method for automatic modulation classification in drone communication systems[J]. Drones, 2023, 7(10): 596.
[3] LIANG T J, BAO H, PAN W G, et al. DetectFormer: category-assisted transformer for traffic scene object detection[J]. Sensors, 2022, 22(13): 4833.
[4] SUN Y, ZHANG Y H, WANG H Y, et al. SES-YOLOv8n: automatic driving object detection algorithm based on improved YOLOv8[J]. Signal, Image and Video Processing, 2024, 18(5): 3983-3992.
[5] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[6] HE K M, GKIOXARI G, DOLLáR P, et al. Mask R-CNN[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2980-2988.
[7] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//Proceedings of the European Conference on Computer Vision. Cham: Springer International Publishing, 2016: 21-37.
[8] REDMON J, FARHADI A. YOLOv3: an incremental impro-vement[J]. arXiv:1804.02767, 2018.
[9] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv:2004. 10934, 2020.
[10] LI C, LI L L, JIANG H L, et al. YOLOv6: a single-stage object detection framework for industrial applications[J]. arXiv: 2209.02976, 2022.
[11] WANG C Y, BOCHKOVSKIY A, LIAO H M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2023: 7464-7475.
[12] WANG C Y, YEH I H, LIAO H Y M. YOLOv9: learning what you want to learn using programmable gradient information[J]. arXiv:2402.13616, 2024.
[13] GE Z, LIU S T, WANG F, et al. YOLOX: exceeding YOLO series in 2021[J]. arXiv:2107.08430, 2021.
[14] ZHENG Q H, SAPONARA S, TIAN X Y, et al. A real-time constellation image classification method of wireless communication signals based on the lightweight network MobileViT[J]. Cognitive Neurodynamics, 2024, 18(2): 659-671.
[15] 马俊燕, 常亚楠. MFE-YOLOX: 无人机航拍下密集小目标检测算法[J]. 重庆邮电大学学报(自然科学版), 2024, 36(1): 128-135.
MA J Y, CHANG Y N. MFE-YOLOX: dense small target detection algorithm under UAV aerial photography[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2024, 36(1): 128-135.
[16] WANG G B, ZHOU K, WANG L Z, et al. Context-aware and attention-driven weighted fusion traffic sign detection network[J]. IEEE Access, 2023, 11: 42104-42112.
[17] YE H C, WANG Y N. Residual transformer YOLO for detec-ting multi-scale crowded pedestrian[J]. Applied Sciences, 2023, 13(21): 12032.
[18] LI X, HE M, LIU Y, et al. SPCS: a spatial pyramid conv-olutional shuffle module for YOLO to detect occludedobject[J]. Complex & Intelligent Systems, 2023, 9(1): 301-315.
[19] SHAO X T, WANG Q, YANG W, et al. Multi-scale feature pyramid network: a heavily occluded pedestrian detection network based on resnet[J]. Sensors, 2021, 21(5): 1820-1820.
[20] 宦涣, 孙艳文. 改进YOLO算法的复杂交通场景目标检测[J]. 西安工程大学学报, 2022, 36(6): 86-92.
HUAN H, SUN Y W. Target detection method in complex traffic scene based on improved YOLO algorithm[J]. Journal of Xi’an Polytechnic University, 2022, 36(6): 86-92.
[21] JIANG H B, REN J H, LI A X. 3D object detection under urban road traffic scenarios based on dual-layer voxel features fusion augmentation[J]. Sensors, 2024, 24(11): 3267.
[22] LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 936-944.
[23] LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 8759-8768.
[24] WU T Y, TANG S, ZHANG R, et al. CGNet: a light-weight context guided network for semantic segmentation[J]. IEEE Transactions on Image Processing, 2020, 30: 1169-1179.
[25] GEIGER A, LENZ P, URTASUN R. Are we ready for autonomous driving? The KITTI vision benchmark suite[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2012: 3354-3361.
[26] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2999-3007.
[27] TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 10778-10787.
[28] ZHOU X Y, WANG D Q, KR?HENBüHL P. Objects as points[J]. arXiv:1904.07850, 2019.