AAL-Net: A Lightweight Detection Method for Road Surface Defects Based on Attention and Data Augmentation
Source abstract
Abstract
The pothole is a common road defect that seriously affects traffic efficiency and personal safety. Road evaluation and maintenance and automatic driving take pothole detection as their main research part. In the above scenarios, accuracy and real-time pothole detection are the most important. However, the current pothole detection methods can not meet the accuracy and real-time requirements of pothole detection due to their multiple parameters and volume. To solve these problems, we first propose a lightweight one-stage object detection network, the AAL-Net. In the network, we design an LF (lightweight feature extraction) module and use the NAM (Normalization-based Attention Module) attention module to ensure the accuracy and real time of the pothole detection process. Secondly, we make our own pothole dataset for pothole detection. Finally, in order to simulate the real road scene, we design a data augmentation method to further improve the detection accuracy and robustness of the AAL-Net. The metrics F1 and GFLOPs show that our method is better than other deep learning models in the self-made dataset and the pothole600 dataset and can well meet the accuracy and real-time requirements of pothole detection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Asphalt Pavement Performance Evaluation · Vehicle License Plate Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
Funding
Taishan Scholars Program
References (51)
Show source-provided references
1. Road Damage Detection Based on Unsupervised Disparity Map Segmentation10.1109/TITS.2019.2947206 ↗
2. Kim, Y.-M., Kim, Y.-G., Son, S.-Y., Lim, S.-Y., Choi, B.-Y., and Choi, D.-H. (2022). Review of Recent Automated Pothole-Detection Methods. Appl. Sci., 12.10.3390/app12115320 ↗
3. Park, S.-S., Tran, V.-T., and Lee, D.-E. (2021). Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection. Appl. Sci., 11.10.3390/app112311229 ↗
4. PotNet: Pothole Detection for Autonomous Vehicle System Using Convolutional Neural Network10.1049/ell2.12062 ↗
5. Sattar, S., Li, S., and Chapman, M. (2018). Road Surface Monitoring Using Smartphone Sensors: A Review. Sensors, 18.10.3390/s18113845 ↗
6. Du, R., Qiu, G., Gao, K., Hu, L., and Liu, L. (2020). Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor. Sensors, 20.10.3390/s20020451 ↗
7. Stereo-Based 3D Reconstruction of Potholes by a Hybrid, Dense Matching Scheme10.1109/JSEN.2019.2898375 ↗
8. Automated Pixel-Level Pavement Distress Detection Based on Stereo Vision and Deep Learning10.1016/j.autcon.2021.103788 ↗
9. Baek, J.-W., and Chung, K. (2020). Pothole Classification Model Using Edge Detection in Road Image. Appl. Sci., 10.10.3390/app10196662 ↗
10. Pothole Detection Using Location-Aware Convolutional Neural Networks10.1007/s13042-020-01078-7 ↗
11. Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery10.1109/JSTARS.2018.2865528 ↗
12. Salaudeen, H., and Çelebi, E. (2022). Pothole Detection Using Image Enhancement GAN and Object Detection Network. Electronics, 11.10.3390/electronics11121882 ↗
13. Arya, D., Maeda, H., Kumar Ghosh, S., Toshniwal, D., Omata, H., Kashiyama, T., and Sekimoto, Y. (2020, January 10). Global Road Damage Detection: State-of-the-Art Solutions. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.10.1109/BigData50022.2020.9377790 ↗
14. Detection and Segmentation of Cement Concrete Pavement Pothole Based on Image Processing Technology
15. Masihullah, S., Garg, R., Mukherjee, P., and Ray, A. (2021, January 10). Attention Based Coupled Framework for Road and Pothole Segmentation. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.10.1109/ICPR48806.2021.9412368 ↗
16. Fan, J., Bocus, M.J., Hosking, B., Wu, R., Liu, Y., Vityazev, S., and Fan, R. (2021, January 11). Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection. Proceedings of the 2021 IEEE International Conference on Autonomous Systems (ICAS), Montreal, QC, Canada.10.1109/ICAS49788.2021.9551165 ↗
17. Anand, S., Gupta, S., Darbari, V., and Kohli, S. (2018, January 10–13). Crack-Pot: Autonomous Road Crack and Pothole Detection. Proceedings of the 2018 Digital Image Computing: Techniques and Applications (DICTA), IEEE, Canberra, Australia.10.1109/DICTA.2018.8615819 ↗
18. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
19. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks10.1109/TPAMI.2016.2577031 ↗
20. Dai, J., Li, Y., He, K., and Sun, J. (2016, January 5–10). R-FCN: Object Detection via Region-Based Fully Convolutional Networks. Proceedings of the Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain.
21. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
22. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
23. Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv.
24. Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv.
25. SSD: Single Shot MultiBox Detector10.1007/978-3-319-46448-0_2 ↗
26. Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017, January 22–29). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.324 ↗
27. Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv.
28. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
29. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv.
30. Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18–23). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00716 ↗
31. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design10.1007/978-3-030-01264-9_8 ↗
32. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv.
33. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18–22). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
34. Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., and Pang, R. (November, January 27). Searching for MobileNetV3. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.10.1109/ICCV.2019.00140 ↗
35. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13–19). GhostNet: More Features from Cheap Operations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.00165 ↗
36. Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
37. Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18–23). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00913 ↗
38. Tan, M., Pang, R., and Le, Q.V. (2020, January 14–19). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01079 ↗
39. A Model for Surface Defect Detection of Industrial Products Based on Attention Augmentation
40. A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation10.1155/2022/6174255 ↗
41. Wang, G., Li, Q., Wang, L., Zhang, Y., and Liu, Z. (2019). Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks. Electronics, 8.10.3390/electronics8111354 ↗
42. A New Lightweight Deep Neural Network for Surface Scratch Detection10.1007/s00170-022-10335-8 ↗
43. Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E. (2018, January 18–23). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00745 ↗
44. Park, J., Woo, S., Lee, J.-Y., and Kweon, I.S. (2018). BAM: Bottleneck Attention Module. arXiv.
45. CBAM: Convolutional Block Attention Module10.1007/978-3-030-01234-2_1 ↗
46. Liu, Z., Wang, L., Wu, W., Qian, C., and Lu, T. (2021, January 10–17). TAM: Temporal Adaptive Module for Video Recognition. Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.10.1109/ICCV48922.2021.01345 ↗
47. Wang, C.-Y., Mark Liao, H.-Y., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., and Yeh, I.-H. (2020, January 14–19). CSPNet: A New Backbone That Can Enhance Learning Capability of CNN. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.10.1109/CVPRW50498.2020.00203 ↗
48. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14–19). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01155 ↗
49. Liu, Y., Shao, Z., Teng, Y., and Hoffmann, N. (2021). NAM: Normalization-Based Attention Module. arXiv.
50. We Learn Better Road Pothole Detection: From Attention Aggregation to Adversarial Domain Adaptation
51. (2023, January 14). Available online: https://www.kaggle.com/datasets/zchengcheng/pothole-datasets.