FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-110217

Research Article |

YOLO Based Deep Learning Model for Segmenting the Color Images

Author(s): D. Rasi1*, M. AntoBennet2, P. N. Renjith3, M. R. Arun4 and D. Vanathi5

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 2

Publisher : FOREX Publication

Published : 30 May 2023

e-ISSN : 2347-470X

Page(s) : 359-370

DOI: https://doi.org/10.37391/IJEER.110217

Abstract

The first stage is to extract fine details from a picture using Red Green Blue (RGB) colour space is colour image segmentation. Most grayscale and colour picture segmentation algorithms use original or updated fuzzy c-means (FCM) clustering. However, due to two factors, the majority of these methods are inefficient and fail to produce the acceptable segmentation results for colour photos. The inclusion of local spatial information often results in a high level of computational complexity due to the repetitive distance computation between clustering centres and pixels within a tiny adjacent window. The second reason is that a typical neighbouring window tends to mess up the local spatial structure of images. Color picture segmentation has been improved by introducing Deep Convolution Neural Networks (CNNs) for object detection, classification and semantic segmentation. This study seeks to build a light-weight for object detector that uses a depth and colour image from a publically available dataset to identify objects in a scene. It's likely to output in the depth way by expanding the YOLO network's network architecture. Using Taylor based Cat Salp Swarm algorithm (TCSSA), the weight of the suggested model is modified to improve the accuracy of region extraction findings. It is possible to test the detector's efficacy by comparing it to various datasets. Testing showed that the suggested model is capable of segmenting input into multiple metrics using bounding boxes. The results shows that the proposed model achieved 0.20 of Global Consistency Error (GCE) and 1.85 of Variation of Information (VOI) on BSDS500 dataset, where existing techniques achieved nearly 1.96 to 1.86 of VOI and 0.25 to 0.22 of GCE for the same dataset.

Keywords: Convolutional Neural Network, Color Image Segmentation, Computational Complexity, Fuzzy C-Means, Grayscale, Taylor based Cat Salp Swarm.




D. Rasi*, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India; Email: rasid@skcet.ac.in

M. AntoBennet, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, India; Email: drmantobenet@veltech.edu.in

P. N. Renjith, Department of Computer Science, Vellore Institute of Technology, Chennai, India; Email: renjith.pn@vit.ac.in

M. R. Arun, Department of Electronics and Communication Engineering Veltech Rangarajan Dr. Sagunthala R & D institute of Science and Technology, Chennai, India; Email: mrarunresearch@gmail.com

D. Vanathi, Department of Computer Science and Engineering, Nandha Engineering College, Erode, India; Email: vanathi.d@nandhaengg.org

    [1] F. Yang, H. Fan, P. Chu, E. Blasch, H. Ling, Clustered object detection in aerial images, in: Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 8311–8320. [Cross Ref]
    [2] M. Hashemzadeh, N. Farajzadeh, Combining keypoint-based and segment-based features for counting people in crowded scenes, Inform. Sci. 345 (2016) 199-216, http://dx.doi.org/10.1016/j.ins.2016.01.060, 2016/06/01/ 2016. [Cross Ref]
    [3] M. Hashemzadeh, A. Zademehdi, Fire detection for video surveillance applications using ICA K-medoids-based color model and efficient spatio-temporal visual features, Expert Syst. Appl. 130 (2019) 60–78, http://dx.doi.org/10.1016/j.eswa.2019.04.019, 09/15/ 2019. [Cross Ref]
    [4] M. Hashemzadeh, B. Adlpour Azar, Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods, Artif. Intell. Med. 95 (2019) 1–15, http://dx.doi.org/10.1016/j.artmed.2019.03.001, /04/01/ 2019. [Cross Ref]
    [5] S. Wazarkar, B.N. Keshavamurthy, A survey on image data analysis through clustering techniques for real world applications, J. Vis. Commun. Image Represent. 55 (2018) 596–626, http://dx.doi.org/10.1016/j.jvcir.2018.07.009,/08/01/ 2018. [Cross Ref]
    [6] L. He, S. Huang, Modified firefly algorithm based multilevel thresholding for color image segmentation, Neurocomputing 240 (2017) 152-174, http://dx.doi.org/10.1016/j.neucom.2017.02.040, /05/31/ 2017. [Cross Ref]
    [7] S. Pare, A. Kumar, V. Bajaj, G.K. Singh, A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve, Appl. Soft Comput. 47 (2016) 76–102, http://dx.doi.org/10.1016/j.asoc.2016.05.040, /10/01/2016. [Cross Ref]
    [8] L. Feng, H. Li, Y. Gao, Y. Zhang, A color image segmentation method based on region salient color and fuzzy C-means algorithm, Circuits Systems Signal Process. 39 (2) (2020) 586–610, http://dx.doi.org/10.1007/s00034-019-01126-w, 02/01 2020. [Cross Ref]
    [9] Z. Zhou, X. Zhao, S. Zhu, K-harmonic means clustering algorithm using feature weighting for color image segmentation, Multimedia Tools Appl. 77 (12) (2018) 15139–15160, http://dx.doi.org/10.1007/s11042-017-5096-9. [Cross Ref]
    [10] T.R. Farshi, J.H. Drake, E. Özcan, A multimodal particle swarm optimization- based approach for image segmentation, Expert Syst. Appl. 149 (2020) 113233, http://dx.doi.org/10.1016/j.eswa.2020.113233, 2020/07/01. [Cross Ref]
    [11] F. Zhao, Z. Zeng, H. Liu, R. Lan, J. Fan, Semisupervised approach to surrogate-assisted multiobjective kernel intuitionistic fuzzy clustering algorithm for color image segmentation, IEEE Trans. Fuzzy Syst. 28 (6) (2020) 1023–1034, http://dx.doi.org/10.1109/TFUZZ.2020.2973121. [Cross Ref]
    [12] A.G. Oskouei, M. Hashemzadeh, B. Asheghi, M.-A. Balafar, CGFFCM: CLuster-weight and group-local feature-weight learning in fuzzy C-means clustering algorithm for color image segmentation, Appl. Soft Comput. (2021) 108005, http://dx.doi.org/10.1016/j.asoc.2021.108005, 2021/10/26. [Cross Ref]
    [13] Deepa, S.N., Rasi, D., Global biotic cross-pollination algorithm enhanced with evolutionary strategies for color image segmentation. Soft Computing A Fusion of Foundations, Methodologies and Applications, Springer 23(8), 2545–2559 (2019). [Cross Ref]
    [14] Deepa, S.N., Rasi, D., Optimized deep learning neural network model for doubly fedinduction generator in wind energy conversion systems. Soft Computing A Fusion of Foundations, Methodologies and Applications, Springer 23(18), 8453-8470 (2019). [Cross Ref]
    [15] Garcia-Lamont, F., Cervantes, J., López, A., Rodriguez, L.: Segmentation of images by color features: a survey. Neurocomputing 292(1), 1–27 (2018). [Cross Ref]
    [16] Markchom, T., Lipikorn, R.: Thin cloud removal using local minimization and logarithm image transformation in HSI color space. In: The 4th International Conference on Front Signal Process, Sept, Poitiers, France. IEEE (2018). [Cross Ref]
    [17] Okur, E.; Turkan, M. A survey on automated melanoma detection. Eng. Appl. Artif. Intell. 2018, 73, 50–67.
    [18] Kang, C., Wu, C. and Fan, J., 2021. Entropy-based circular histogram thresholding for color image segmentation. Signal, Image and Video Processing, 15(1), pp.129-138. [Cross Ref]
    [19] Sathya, P.D., Kalyani, R. and Sakthivel, V.P., 2021. Color image segmentation using Kapur, Otsu and minimum cross entropy functions based on exchange market algorithm. Expert Systems with Applications, 172, p.114636. [Cross Ref]
    [20] Rasi, D., Deepa, S.N. Hybrid optimization enabled deep learning model for colour image segmentation and classification. Neural Comput & Applic 34, pp.21335–21352 (2022). [Cross Ref]
    [21] Wang, S., Sun, K., Zhang, W. and Jia, H., 2021. Multilevel thresholding using a modified ant lion optimizer with opposition-based learning for color image segmentation. Math. Biosci. Eng, 18(4), pp.3092-3143. [Cross Ref]
    [22] Wei, T., Wang, X., Li, X. and Zhu, S., 2022. Fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking. Engineering Applications of Artificial Intelligence, 110, p.104672. [Cross Ref]
    [23] Wu, C. and Zhang, X., 2022. Total Bregman divergence-driven possibilistic fuzzy clustering with kernel metric and local information for grayscale image segmentation. Pattern Recognition, 128, p.108686. [Cross Ref]
    [24] Takahashi, M., Ji, Y., Umeda, K. and Moro, A., 2020, December. Expandable YOLO: 3D object detection from RGB-D images. In 2020 21st International Conference on Research and Education in Mechatronics (REM) (pp. 1-5). IEEE. [Cross Ref]
    [25] Xianbao, C., Guihua, Q., Yu, J. and Zhaomin, Z., 2021. An improved small object detection method based on Yolo V3. Pattern Analysis and Applications, 24(3), pp.1347-1355. [Cross Ref]
    [26] Magalhães SA, Castro L, Moreira G, Dos Santos FN, Cunha M, Dias J, Moreira AP. Evaluating the single-shot multibox detector and YOLO deep learning models for the detection of tomatoes in a greenhouse. Sensors. 2021 May 20; 21(10):3569. [Cross Ref]
    [27] Abas SM. A Yolo and convolutional neural network for the detection and classification of leukocytes in leukemia (Doctoral dissertation, Polytechnic University).
    [28] Hurtik, P., Molek, V., Hula, J., Vajgl, M., Vlasanek, P. and Nejezchleba, T., 2022. Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3. Neural Computing and Applications, 34(10), pp.8275-8290. [Cross Ref]
    [29] Su, Y., Liu, Q., Xie, W. and Hu, P., 2022. YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms. Computer Methods and Programs in Biomedicine, p.106903. [Cross Ref]
    [30] Jainulabudeen, A. and Surputheen, M.M., 2022. Novel Two-Level Randomized Sector-based Routing to Maintain Source Location Privacy in WSN for IoT. IJCSNS, 22(3), p.285.
    [31] Vaiyapuri, T., Alaskar, H., Parvathi, R., Pattabiraman, V. and Hussain, A., 2022. Cat Swarm Optimization-Based Computer-Aided Diagnosis Model for Lung Cancer Classification in Computed Tomography Images. Applied Sciences, 12(11), p.5491. [Cross Ref]
    [32] Zhang, H., Liu, T., Ye, X., Heidari, A.A., Liang, G., Chen, H. and Pan, Z., 2022. Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems. Engineering with Computers, pp.1-35. [Cross Ref]
    [33] Yue, X., Wang, Q., He, L., Li, Y. and Tang, D., 2022. Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO. Applied Sciences, 12(13), p.6823. [Cross Ref]
    [34] Hurtik, P., Molek, V., Hula, J., Vajgl, M., Vlasanek, P. and Nejezchleba, T., 2022. Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3. Neural Computing and Applications, 34(10), pp.8275-8290. [Cross Ref]
    [35] D. Martin, C. Fowlkes, J. Malik, Learning to detect natural image boundaries using local brightness, color, and texture cues, Transactions on Pattern Analysis and Machine Intelligence 26 (5) (2004) 530–549. doi:10.1109/TPAMI.2004.1273918. [Cross Ref]
    [36] D. R. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in IEEE International Conference on Computer Vision, 2001, pp. 416–423. [Cross Ref]
    [37] Minaee, S., Boykov, Y.Y., Porikli, F., Plaza, A.J., Kehtarnavaz, N. and Terzopoulos, D., 2021. Image segmentation using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence. [Cross Ref]
    [38] Mohammdian-Khoshnoud, M., Soltanian, A.R., Dehghan, A. and Farhadian, M., 2022. Optimization of fuzzy c-means (FCM) clustering in cytology image segmentation using the gray wolf algorithm. BMC Molecular and Cell Biology, 23(1), pp.1-9. [Cross Ref]
    [39] Sivapriya, G., Praveen, V., Gowri, P., Saranya, S., Sweetha, S. and Shekar, K., 2022. Segmentation of Hard exudates for the detection of Diabetic Retinopathy with RNN based sematic features using fundus images. Materials Today: Proceedings. [Cross Ref]
    [40] Swetha S , S. Saranya and M. Devaraju, Skin Cancer Detection and Segmentation Using Convolutional Neural Network Models, Volume 10, Issue 4,2022 (pp. 984-987), IJEER. [Cross Ref]
    [41] Moussaoui, H., Benslimane, M. and El Akkad, N., 2022. Image segmentation approach based on hybridization between K-means and mask R-CNN. In WITS 2020 (pp. 821-830). Springer, Singapore. [Cross Ref]
    [42] Chen, S., Zhou, Q. and Zou, H., 2022. A Novel Un-Supervised GAN for Fundus Image Enhancement with Classification Prior Loss. Electronics, 11(7), p.1000. [Cross Ref]

D. Rasi, M. AntoBennet, P. N. Renjith, M. R. Arun and D. Vanathi (2023), YOLO Based Deep Learning Model for Segmenting the Color Images. IJEER 11(2), 359-370. DOI: 10.37391/IJEER.110217.