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Volume 13 | Issue 8 | Year 2026 | Article Id. IJEEE-V13I8P104 | DOI : https://doi.org/10.14445/23488379/IJEEE-V13I8P104

Analysis of Edge Detectors based on Performance Evaluation Metrics

Wazir, Rajeshwar Dass

Received Revised Accepted Published
26 May 2026 23 Jun 2026 24 Jul 2026 25 Aug 2026

Citation :

Wazir, Rajeshwar Dass, "Analysis of Edge Detectors based on Performance Evaluation Metrics," International Journal of Electrical and Electronics Engineering, vol. 13, no. 8, pp. 35-46, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I8P104

Abstract

A joint comparison of the outcomes of edge detection techniques provides a more thorough knowledge of the capabilities of the algorithms to identify image edges. Edge detection methods are classified as conventional edge detection, soft computing edge detection and deep learning edge detection strategies. Detection efficiency is evaluated by means of a comprehensive analysis that includes F1-score and Intersection over Union (IoU). Deep learning-based models have proven to be more efficient with regard to F1-scores due to better feature extraction. High PSNR indicates good agreement between detected boundaries and actual objects. In the course of conducted experiments, Clip-MobileNetV2-Unet has shown better results concerning Recall, Precision, F1-Score/mDC, Accuracy, PSNR (dB), and mIoU. All these metrics make up a complex evaluation strategy. From this study, it is found that deep learning algorithms perform better in terms of accuracy and reliability compared to other approaches, while traditional detectors are fast and suitable for simple real-time purposes.

Keywords

Edge detectors, Performance evaluation metrics, Machine Learning, Deep Learning, Soft Computing.

References

  1. Saeedeh Abasi, Mohammad A. Tehran, and Mark D. Fairchild, “Colour Metrics for Image Edge Detection,” Color Research and Application, vol. 45, no. 4, pp. 632-643, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
  2. Ramya Keerthi M.R., and T.P. Mithun, “Edge Connectivity Techniques for Image Analysis - A Survey,” International Research Journal of Engineering and Technology, vol. 7, no. 5, pp. 5374- 5379, 2020.
    [Google Scholar]
  3. Shigang Wang, Xianghua Liao, and Guoqiang Wu, “Infrared Image Edge Detection based on Improved Canny Algorithm,” 2021 IEEE 3rd Eurasia Conference on IoT, Communication and Engineering, Yunlin, Taiwan, pp. 280-284, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  4. Vishtasp Meherhomji, and K.B. Ajitha Shenoy, “An Improved Edge Detection Technique,” International Journal of Computational Vision and Robotics, vol. 11, no. 6, pp. 653-670, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  5.  Agnieszka Lisowska, “Efficient Edge Detection Method for Focused Images,” Applied Sciences, vol. 12, no. 22, no. 1-11, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  6. Kgs Muhammad Rizky Alditra Utama, Rusydi Umar, and Anton Yudhana, “Edge Detection Comparative Analysis using Roberts, Sobel, Prewitt, and Canny Methods,” Journal of Technology and Computer Systems, vol. 10, no. 2, pp. 67-71, 2022.
    [Google Scholar]
  7. Yahya Ismail Ibrahim, and Israa Mohammed Alhamdani, “Practical Study for Comparing Edge Detection Filters in Digital Image Processing,” Tikrit Journal of Pure Science, vol. 28, no. 5, pp. 201-215, 2023.
    [CrossRef] [Publisher Link]
  8. Rajshree Kumari, and Divyanshu Chandra, “Real-Time Comparison of Performance Analysis of Various Edge Detection Techniques based on Imagery Data,” Current Journal of Applied Science and Technology, vol. 42, no. 24, pp. 22-31, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  9. Fallah H. Najjar, Ola N. Kadhim, and Salman kadum Abd, “Ant Colony Optimization based Edge Detection Algorithm,” Al-Furat Journal of Innovations in Electronics and Computer Engineering, pp. 479-487, 2024.
    [CrossRef] [Google Scholar]
  10. Min Chen, “A Distributed Ant Colony Optimization Applied in Edge Detection,” Journal of Computer and Communications, vol. 12, no. 8, pp. 161-173, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  11. Abeer Aljohani, “Optimized Convolutional Forest by Particle Swarm Optimizer for Pothole Detection,” International Journal of Computational Intelligence Systems, vol. 17, no. 1, pp. 1-15, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. Fangyan Nie, Mengzhu Liu, and Pingfeng Zhang, “Multilevel Thresholding with Improved Particle Swarm Optimization for Crack Image Segmentation,” Scientific Reports, vol. 14, no. 1, pp. 1-19, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  13. Yuanyuan Zou, Shilong Xu, and Boxuan Chen, “An Improved Zernike Moment Subpixel Edge Detection Algorithm based on Adaptive Threshold,” Internet of Things, Artificial Intelligence and Mechanical Automation, Springer, vol. 641, pp. 211-222, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  14. Wenlong Fu, Mark Johnston, and Mengjie Zhang, “Unsupervised Learning for Edge Detection using Genetic Programming,” 2014 IEEE Congress on Evolutionary Computation, Beijing, China, pp. 117-124, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  15. Y. Ryu et al., “Image Edge Detection using Fuzzy C-Means and Three Directions Image Shift Method,” IAENG International Journal of Computer Science, vol. 45, no. 1, pp. 1-6, 2018.
    [Google Scholar]
  16. Jichao Cui, and Kun Tian, “Edge Detection Algorithm Optimization and Simulation based on Machine Learning Method and Image Depth Information,” IEEE Sensors Journal, vol. 20, no. 20, pp. 11770-11777, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
  17. Caixia Liu, Ruibin Zhao, and Mingyong Pang, “Lung Segmentation based on Random Forest and Multi-Scale Edge Detection,” IET Image Processing, vol. 13, no. 10, pp. 1745-1754, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
  18. Xi Li et al., “Partial Discharge Source Localization in GIS based on Image Edge Detection and Support Vector Machine,” IEEE Transactions on Power Delivery, vol. 34, no. 4, pp. 1795-1802, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
  19. Pablo A. Flores-Vidal et al., “A New Edge Detection Method based on Global Evaluation using Fuzzy Clustering,” Soft Computing, vol. 23, no. 6, pp. 1809-1821, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
  20. Rémi Cogranne et al., “A New Edge Detector based on Parametric Surface Model: Regression Surface Descriptor,” arXiv preprint, pp. 1-21, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
  21. Fangfang Han et al., “Algorithm Design for Edge Detection of High-Speed Moving Target Image under Noisy Environment,” Sensors, vol. 19, no. 2, pp. 1-27, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
  22. Yangyang Liu et al., “Breast Tumours Recognition based on Edge Feature Extraction using Support Vector Machine,” Biomedical Signal Processing and Control, vol. 58, pp. 1-8, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
  23. M. Vilasini, and P. Ramamoorthy, Leaf Recognition using Prewitt Edge Detection and K-NN Classification, New Trends in Computational Vision and Bio-inspired Computing, Coimbatore, India, Springer, pp. 1507-1515, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
  24. Yu Wang et al., “Machine Learning-based Ship Detection and Tracking using Satellite Images for Maritime Surveillance,” Journal of Ambient Intelligence and Smart Environments, vol. 13, no. 5, pp. 361-371, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  25. Pablo Flores-Vidal, Javier Castro, and Daniel Gómez, “Postprocessing of Edge Detection Algorithms with Machine Learning Techniques,” Mathematical Problems in Engineering, vol. 2022, no. 1, pp. 1-12, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  26. Fangsheng Wu et al., “Research on Image Text Recognition based on Canny Edge Detection Algorithm and K-Means Algorithm,” International Journal of System Assurance Engineering and Management, vol. 13, no. 1, pp. 72-80, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  27. G.S.K. Ganesh Prasad et al., “Detection of CKD from CT Scan Images using KNN Algorithm and using Edge Detection,” 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies, Patna, India, pp. 1-4, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  28. Gracieth Cavalcanti Batista et al., “Machine Learning Algorithm Partially Reconfigured on FPGA for an Image Edge Detection System,” Journal of Electronic Science and Technology, vol. 22, no. 2, pp. 1-19, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  29. Xavier Soria et al., “Tiny and Efficient Model for the Edge Detection Generalization,” 2023 IEEE/CVF International Conference on Computer Vision Workshops, Paris, France, pp. 1356-1365, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  30. Yachuan Li et al., “A New Baseline for Edge Detection: Make Encoder-Decoder Great Again,” Signal Processing: Image Communication, vol. 142, pp. 1-12, 2026.
    [    CrossRef] [Google Scholar] [Publisher Link]
  31. Fuping Wang, and Min Zhang, “Deep Learning-based Edge Detection Algorithm for Noisy Images,” Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition, pp. 465-472, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  32. Omar Elharrouss et al., “Refined Edge Detection with Cascaded and High-Resolution Convolutional Network,” Pattern Recognition, vol. 138, pp. 1-10, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  33.  Dongdong Jing et al., “Edge Detection in Dark Industrial Environments,” 2023 9th International Conference on Computer and Communications, Chengdu, China, pp. 1689-1693, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  34. Abdullah Al-Amaren, M. Omair Ahmad, and M.N.S. Swamy, “A Low-Complexity Residual Deep Neural Network for Image Edge Detection,” Applied Intelligence, vol. 53, no. 9, pp. 11282-11299, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  35. Fuzhang Li, and Chuan Lin, “UHNet: An Ultra-Lightweight and High-Speed Edge Detection Network,” arXiv preprint, pp. 1-18, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  36. Jiahao Zhang, Wei Wang, and Jianfei Wang, “Edge Detection in Colored Images using Parallel CNNs and Social Spider Optimization,” Electronics, vol. 13, no. 17, pp. 1-18, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  37. Firas Abedi et al., “Dense Residual Network for Image Edge Detection,” Multimedia Tools and Applications, vol. 83, no. 42, pp. 90227-90242, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  38. Ying An et al., “An Exclusive U-Net for Fine and Crisp Edge Detection,” Multimedia Tools and Applications, vol. 83, no. 18, pp. 54657-54672, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  39. Kanija Muntarina et al., “Deep Learning-based Edge Detection for Random Natural Images,” Neuroscience Informatics, vol. 5, no. 1, pp. 1-10, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  40. Ming Wang, “AttnEdge: An Enhanced Edge Detection Method based on Self-Attention Mechanism,” Fourth International Conference on Computer Vision, Application, and Algorithm, vol. 13486, pp. 438-446, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  41. Wenlin Li et al., “Pixel-Patch Combination Loss for Refined Edge Detection,” International Journal of Machine Learning and Cybernetics, vol. 16, no. 2, pp. 1341-1354, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  42. Xiaodiao Chen et al., “Sufficient Learning: Mining Denser High-Quality Pixel-Level Labels for Edge Detection,” Neural Computing and Applications, vol. 37, no. 14, pp. 8245-8260, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]  
  43. Xi Yang et al., “Textureness-Aware Neural Network for Edge Detection,” Chinese Conference on Pattern Recognition and Computer Vision, Springer, Singapore, pp. 254-268, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  44. Bedrettin Cetinkaya, Sinan Kalkan, and Emre Akbas, “MatchED: Crisp Edge Detection using End-to-End, Matching-based Supervision,” arXiv preprint, pp. 1-27, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]