In order to address the issues of noise sensitivity, edge discontinuity, false positives, and false negatives in image edge detection, this paper proposes an enhanced gravity search algorithm (IGSA). Through experiments on three datasets: CSet8, BSD500, and OTCBVS, the system compared the performance differences between this algorithm and traditional edge detection methods. The results showed that IGSA improved the detection accuracy by 7% on ordinary color images, and by 3.5% on images with added Gaussian white noise. In addition, in infrared images, the edge linear connectivity reaches 1.333, which is 9.6% higher than traditional methods. These results fully demonstrate that the proposed algorithm not only improves the accuracy of edge detection, but also exhibits stronger robustness under noise interference.
ElmiS.ElmiZ. (2022). A robust edge detection technique based on matching pursuit algorithm for natural and medical images. Biomedical Engineering Advances, 4, 100052. https://doi.org/10.1016/j.bea.2022.100052
2.
Engineering. (2019). New engineering findings from south China normal university described (A novel method for image segmentation based on simplified pulse couple neural network and gbest led gravitational search algorithm). News of Science, 182–183.
3.
GuiY.WuJ. (2011). Image edge detection method based on laplacian operator and gray relational degree. Shantou University Journal (Natural Science Edition, 26(02), 69–73.
4.
HadiN.MeysamA.SaeedN. (2021). A nonlinear model predictive controller based on the gravitational search algorithm. Optimal Control Applications and Methods, 42(6), 1734–1761. https://doi.org/10.1002/oca.2757
5.
HoL. V.NguyenD. H.de RoeckG.Bui-TienT.WahabM. A. (2021). Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm. Journal of Zhejiang University-SCIENCE A, 22(6), 467–480. https://doi.org/10.1631/jzus.A2000316
6.
JiaoKPanZ. (2019). A novel method for image segmentation based on simplified pulse couple neural network and gbest led gravitational search algorithm. IEEE ACCESS, 7, 21310–21330. https://doi.org/10.1109/ACCESS.2019.2894301
7.
KangX. (2021). Application research based on improved gravity search algorithm (pp. 1–61). Henan University.
8.
LiH.XuK. (2025). Innovative adaptive edge detection for noisy images using wavelet and Gaussian method. Scientific Reports, 15, 5838. https://doi.org/10.1038/s41598-025-86860-9
9.
LiH.ZhangW. (2019). SVM Flame recognition algorithm based on quantum gravity search algorithm. Electronic Measurement Technology, 42(18), 81–84. https://doi.org/10.19651/j.cnki.emt.1902810
10.
LuM.CaiZ. (2022). An image edge detection method based on slime swarm algorithm. Changjiang Information Communication, 35(03), 71–74.
11.
MaZ.CaiZ.(2023). An image edge detection method based on plant community algorithm. Changjiang Information and Communication, 36(5), 65–67.
12.
MoJ.YanH.LiuJ. (2022). An adaptive sub-pixel edge detection method based on improved zernike moment. International Journal of Wireless and Mobile Computing, 22(2), 140–147. https://doi.org/10.1504/IJWMC.2022.123314
RojalinP.SibaramaP.SahuP. K. (2021). Implementation of gradient gravitational search algorithm towards conformational search. Computational and Theoretical Chemistry, 1208(2022), 113550. https://doi.org/10.1016/j.comptc.2021.113550
15.
TangY.XuZ.HuangX.ZhuTLiL. (2017). Research on edge detection of lane line image based on roberts operator. Journal of Liaoning University of Technology (Natural Science Edition, 37(06), 383–386+390. https://doi.org/10.15916/j.issn1674-3261.2017.06.009
16.
ThiagarajanK.Manapakkam AnandanM.StatecznyA.Bidare DivakarachariP.Kivudujogappa LingappaH. (2021). Satellite image classification using a hierarchical ensemble learning and correlation coefficient-based gravitational search algorithm. Remote Sensing, 13(21), 4351. https://doi.org/10.3390/rs13214351
17.
WangJ.ChenJ. (2022). Subpixel edge detection algorithm based on improved Gaussian fitting and canny operator. Academic Journal of Computing Information Science, 5.0(7.0), 33–39. https://doi.org/10.25236/AJCIS.2022.050706
18.
WangY.TanZ.ChenY. (2021). An adaptive gravitational search algorithm for multilevel image thresholding. The Journal of Supercomputing, 77(9), 1–18. https://doi.org/10.1007/s11227-021-03706-7
19.
WangF.ZhangM.GongL. (2016). Improved roberts image edge detection algorithm. Journal of Detection and Control, 38(02), 88–92.
20.
XingZ.ShangJ. (2023). Improved sobel algorithm image edge detection system based on FPGA. Software, 44(03), 5–9.
21.
YeY.XuK.,HuangY.YiR.CaiZ. (2024). Diffusionedge: Diffusion probabilistic model for crisp edge detection. In Proceedings of the thirty-eighth AAAI conference on artificial intelligence and thirty-sixth conference on innovative applications of artificial intelligence and fourteenth symposium on educational advances in artificial intelligence (AAAI'24/IAAI'24/EAAI'24) (Vol. 38, p. 742, 6675–6683). AAAI Press.
22.
ZhangP.LiT.LiR.LuS, (2022). An improved sobel image edge detection algorithm and its implementation. Television Technology, 46(05), 42–45. https://doi.org/10.16280/j.videoe.2022.05.009
