In the modern automotive industry, the demand for road crack detection is increasing, but the current methods either over-pursuing the lightweight, which leads to the performance unchanged or even decreased or over-pursuing the performance index, and do not consider the limitation of computational resources, this paper proposes a model BL-YOLOV11, which achieves a balance between the performance and the improvement of the utilization of computational resources, and the model is better than the original model YOLOV11. In terms of performance, YOLOV11 improves Precision by 5%, Recall by 4%, and mAP@50 and mAP@50:90 by 4% and 2%, respectively, with only a 3% increase in computational resources, which is an effective improvement in balancing computational efficiency and performance.
KumarPSViswanathanVBharathiB. Analysis of road accident. IOP Conf Ser Mater Sci Eng2019; 590(1): 012029. DOI: 10.1088/1757-899X/590/1/012029.
3.
ZhouYGuoXHouF, et al.Review of intelligent road defects detection technology. Sustainability2022; 14(10): 6306. DOI: 10.3390/su14106306.
4.
HuangSLiuCGodaK. Applicability of smooth particle hydrodynamics method to large sliding deformation of saturated slopes under earthquake action. Chin J Geotech Eng2023; 45(2): 336–344. DOI: 10.11779/CJGE20211274.
5.
WangXGaoHJiaZ, et al.BL-YOLOv8: an improved road defect detection model based on YOLOv8. Sensors2023; 23(20): 8361. DOI: 10.3390/s23208361.
6.
GeXMarianoV. Commercial applications of 3D human pose estimation in biotechnology: a two-stage fusion with multi-feature integration approach. J. Commer. Biotechnol2024; 29(3): 402–415. DOI: 10.5912/jcb1880.
7.
ZouX. A review of object detection techniques. Proceeding 2019 International Conference Smart Grid Electrical. Autom. (ICSGEA), 2019, pp. 251–254. DOI: 10.1109/ICSGEA.2019.00065.
8.
JiangJZhongXChangZ, et al.Object detection of transmission tower based on DPM. Proceeding 4th International Conference Information Technology Electrical Engineering. Association for Computing Machinery, 2022. DOI: 10.1145/3513142.3513167.
9.
LiQNiazUMerialdoB. An improved algorithm on viola-jones object detector. Proceeding 10th International Workshop Content-Based Multimedia Indexing (CBMI). IEEE, 2012, pp. 1–6. DOI: 10.1109/CBMI.2012.6269796.
10.
XuPHuangLSongY. An optimal method based on HOG-SVM for fault detection. Multimed Tools Appl2022; 81(5): 6995–7010. DOI: 10.1007/s11042-022-12020-0.
11.
ZhangJZhaoXHuangY, et al.Semantic windows mining in sliding window based object detection. Proceeding 21st International Conference Pattern Recognition (ICPR), 2012, pp. 3264–3267. https://ieeexplore.ieee.org/abstract/document/6460861
12.
ChhillarISinghA. A feature engineering-based machine learning technique to detect and classify lung and colon cancer from histopathological images. Med Biol Eng Comput2024; 62(3): 913–924. DOI: 10.1007/s11517-023-02984-y.
13.
ChenWLuoJZhangF, et al.A review of object detection: datasets, performance evaluation, architecture, applications and current trends. Multimed Tools Appl2024; 83(24): 65603–65661. DOI: 10.1007/s11042-023-17949-4.
14.
XuXLeiYYangF. Railway subgrade defect automatic recognition method based on improved faster R-CNN. Sci Program2018; 2018(4832972): 1–12. DOI: 10.1155/2018/4832972.
15.
XuYLiDXieQ, et al.Automatic defect detection and segmentation of tunnel surface using modified mask R-CNN. Measurement2021; 178: 109316. DOI: 10.1016/j.measurement.2021.109316.
16.
LinT-YGoyalPGirshickR, et al.Focal Loss for Dense Object Detection, 2018. arXiv:1708.02002, Available: https://arxiv.org/abs/1708.02002
17.
YanKZhangZ. Automated asphalt highway pavement crack detection based on deformable single shot multi-box detector under a complex environment. IEEE Access2021; 9: 150925–150938. DOI: 10.1109/ACCESS.2021.3125703.
18.
XieYHuWXieS, et al.Surface defect detection algorithm based on feature-enhanced YOLO. Cognit Comput2023; 15(2): 565–579. DOI: 10.1007/s12559-022-10061-z.
19.
ZhongSChenCLuoW, et al.DDVC-YOLOv5: an improved YOLOv5 model for road defect detection. IEEE Access2024; 12: 134008–134019. DOI: 10.1109/ACCESS.2024.3453914.
20.
YangZYuZGuanQ, et al.MHAF-YOLO: Multi-Branch Heterogeneous Auxiliary Fusion YOLO for Accurate Object Detection, 2025. arXiv:2502.04656.
21.
KhanamRHussainM. YOLOv11: An Overview of the Key Architectural Enhancements, 2024. arXiv:2410.17725.
22.
WangAChenHLinZ, et al.RepViT: Revisiting Mobile CNN from ViT Perspective, 2024. arXiv:2307.09283.
23.
EsserPRombachROmmerB. Taming Transformers for High-Resolution Image Synthesis, 2021. arXiv:2012.09841.
24.
OuyangDHeSZhangG, et al.Efficient multi-scale attention module with cross-spatial learning. Proceeding ICASSP 2023 - 2023 IEEE International Conference Acoustics, Speech, Signal Processing (ICASSP). IEEE, 2023, pp. 1–5. DOI: 10.1109/ICASSP49357.2023.10096516.
25.
SelvarajuRRDasAVedantamR, et al.Grad-CAM: Why Did You Say That?2017. arXiv:1611.07450.
