Sharpness-Based Distance Detection
Source abstract
Abstract
With the advancement of artificial intelligence, visual ranging has become a widely researched field. This paper introduces a novel method for distance measurement by evaluating the sharpness of objects in the current frame. It is well known that the image is sharpest at the camera’s focal point and becomes blurry when moving away from it. Leveraging this characteristic, this study utilizes sharpness to achieve distance measurement. Initially, the specific orientation of the target object is identified and located. The image area of the target object is cropped in a certain direction, and its image quality is assessed through sharpness calculation. Subsequently, the relationship between sharpness and distance values is analysed statistically, and a function curve depicting their correlation is plotted. Consequently, the distance between the target object and the detector can be determined based on sharpness with an extremely small margin of error using this method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image and Video Retrieval Techniques · Video Surveillance and Tracking Methods · Face and Expression Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Engineering Physics, Shenzhen University of Technology, Shenzhen 518118, China
College of Applied Technology, Shenzhen University, Shenzhen 518060, China
Funding
the Shenzhen Science and Technology Program
References (45)
Show source-provided references
1. Chen, Y., Meng, Z., Liu, J., and Jiang, H. (2011, January 15–17). High-precision infrared pulse laser ranging for active vehicle anti-collision application. Proceedings of the International Conference on Electric Information & Control Engineering, Wuhan, China.
2. Infrared laser ranging in auto adaptive cruise control system
3. IEEE Staff (2010). 2010 Intl. Symposium on Spread Spectrum Techniques and Applications, IEEE.
4. Parviainen, J., Lopez, M.A.V., Pekkalin, O., Hautamaki, J., Collin, J., and Davidson, P. (2009, January 8–10). Using Doppler Radar and MEMS Gyro to Augment DGPS for Land Vehicle Navigation. Proceedings of the IEEE Control Applications, St. Petersburg, Russia.10.1109/CCA.2009.5281057 ↗
5. Barford, L. (2010, January 3–6). Parallel Transition Localization. Proceedings of the Instrumentation and Measurement Technology Conference (I2MTC), 2010 IEEE, Austin, TX, USA.10.1109/IMTC.2010.5487990 ↗
6. Vision-based vehicle detection for a driver assistance system10.1016/j.camwa.2010.08.081 ↗
7. Research on lengthways vehicle distance measurement system of monocular photograph based on computer vision
8. Varuna, D.S., Jamie, R., and Ahmet, K. (2018). Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots. Sensors, 18.10.3390/s18082730 ↗
9. An on-board vision sensor system for small unmanned vehicle applications10.1007/s00138-012-0413-9 ↗
10. Frag, A.L., Yu, X.R., Yi, W.J., and Saniie, J. (2022, January 19–21). Indoor Navigation System for Visually Impaired People using Computer Vision. Proceedings of the 2022 IEEE International Conference on Electro Information Technology (eIT), Mankato, MN, USA.10.1109/eIT53891.2022.9813919 ↗
11. Research on the Emergency Obstacle Avoidance Strategy of Intelligent Vehicles Based on a Safety Distance Model10.1109/ACCESS.2023.3238056 ↗
12. Nunes, D., Fortuna, J., Damas, B., and Ventura, R. (2022, January 29–30). Real-time Vision Based Obstacle Detection in Maritime Environments. Proceedings of the 2022 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Santa Maria da Feira, Portugal.10.1109/ICARSC55462.2022.9784771 ↗
13. Shi, Z., Li, Z., Che, S., Gao, M., and Tang, H. (2023). Visual Ranging Based on Object Detection Bounding Box Optimization. Appl. Sci., 13.10.3390/app131910578 ↗
14. Zhu, M., Yu, L., Wang, Z., Ke, Z., and Zhi, C. (2023). Review: A Survey on Objective Evaluation of Image Sharpness. Appl. Sci., 13.10.3390/app13042652 ↗
15. Image quality assessment based on deep learning with FPGA implementation10.1016/j.image.2020.115780 ↗
16. A sub-regional compression method for greenhouse images based on CNN image quality assessment10.1111/jfpp.16992 ↗
17. TTL-IQA: Transitive transfer learning based no-reference image quality assessment10.1109/TMM.2020.3040529 ↗
18. Liu, Z., Hong, H., Gan, Z., Wang, J., and Chen, Y. (2022). An Improved Method for Evaluating Image Sharpness Based on Edge Information. Appl. Sci., 12.10.3390/app12136712 ↗
19. Blind Image Quality Evaluation Method Based on Cyclic Generative Adversarial Network10.1109/ACCESS.2024.3375940 ↗
20. Yang, R., Yu, S.Y., Yao, Q.H., Huang, J.M., and Ya, F.M. (2023). Vehicle Distance Measurement Method of Two-Way Two-Lane Roads Based on Monocular Vision. Appl. Sci., 13.10.3390/app13063468 ↗
21. Study on small target pedestrian detection and ranging based on monocular vision
22. Zhang, Y., Gong, Y., and Chen, X. (2024). Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision. World Electr. Veh. J., 15.10.3390/wevj15020062 ↗
23. Zhang, C., Guo, C., and Li, Y. (2024). Research on Aircraft Door ldentification and Position Method Based on lmproved YOLOv5. Comput. Meas. Cont., 1–9. Available online: http://kns.cnki.net/kcms/detail/11.4762.TP.20230816.1145.022.html.
24. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv.
25. Research on the Application of Helmet Detection Based on YOLOv410.4236/jcc.2022.108009 ↗
26. Contributors, M. (2023, August 15). YOLOv7 by MMYOLO. Available online: https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov7.
27. Lan, W., Dang, J., Wang, Y., and Wang, S. (2018, January 5–8). Pedestrian detection based on YOLO network model. Proceedings of the 2018 IEEE International Conference on Mechatronics and Automation (ICMA), Jilin, China.10.1109/ICMA.2018.8484698 ↗
28. Adaptive fusion of multi-scale YOLO for pedestrian detection10.1109/ACCESS.2021.3102600 ↗
29. Object detection for autonomous vehicles with sensor-based technology using yolo10.18201/ijisae.2022.276 ↗
30. Edge YOLO: Real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles10.1109/TITS.2022.3158253 ↗
31. YOLO-based human action recognition and localization10.1016/j.procs.2018.07.112 ↗
32. Weapons detection for security and video surveillance using CNN and YOLO-v5s
33. Video Analysis in Sports by Lightweight Object Detection Network under the Background of Sports Industry Development10.1155/2022/3844770 ↗
34. Ma, H., Celik, T., and Li, H. (2021, January 26–28). Fer-yolo: Detection and classification based on facial expressions. Proceedings of the Image and Graphics: 11th International Conference, ICIG 2021, Proceedings, Part I 11, Haikou, China.10.1007/978-3-030-87355-4_3 ↗
35. Wang, C.Y., Liao, H.Y.M., and Yeh, I.H. (2022). Designing Network Design Strategies Through Gradient Path Analysis. arXiv.
36. A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS10.3390/make5040083 ↗
37. A review on the attention mechanism of deep learning10.1016/j.neucom.2021.03.091 ↗
38. Wojke, N., Bewley, A., and Paulus, D. (2017, January 17–20). Simple Online and Realtime Tracking with a Deep Association Metric. Proceedings of the IEEE International Conference on Image Processing (ICIP), Beijing, China.10.1109/ICIP.2017.8296962 ↗
39. Sheikh, H.R., Wang, Z., Cormack, L., and Bovik, A.C. (2006, September 08). LIVE Image Quality Assessment Database Release 2. Available online: http://live.ece.utexas.edu/research/quality.
40. Murray, N., Marchesotti, L., and Perronnin, F. (2012, January 16–21). AVA: A large-scaledatabase for aesthetic visual analysis. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.10.1109/CVPR.2012.6247954 ↗
41. Ponomarenko, N., Ieremeiev, O., Lukin, V., Egiazarian, K., Jin, L., Astola, J., Vozel, B., Chehdi, K., Carli, M., and Battisti, F. (2013, January 10–12). Color image database TID2013: Peculiarities and preliminary results. Proceedings of the 4th European Workshop on Visual Information Processing (EUVIP), Paris, France.10.1109/EUVIP.2014.7018376 ↗
42. A New Color Image Database TID2013: Innovations and Results10.1007/978-3-319-02895-8_36 ↗
43. Ruikar, J., and Chaudhury, S. (2023). NITS-IQA Database: A New Image Quality Assessment Database. Sensors, 23.10.3390/s23042279 ↗
44. Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4–9). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.10.1609/aaai.v31i1.11231 ↗
45. Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3–6). ImageNet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, Lake Tahoe, NV, USA.