• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 5, Pages: 331-338

Original Article

Differentiating Stagnant Water from Wet Surface for Detecting Potential Mosquito Breeding Sites in Real Time

Received Date:31 October 2022, Accepted Date:14 December 2022, Published Date:04 February 2023


Objective: Mosquito breeding site detection is crucial due to the colorization of water. Most systems fail to identify different types of stagnant water; hence, accurate water identification is essential. This study aims to devise an approach that can help increase the accuracy of detecting and distinguishing stagnant water from that of other wet surfaces. Methods: This work has proposed a technique using anchor boxes to reduce misclassification for detecting stagnant water. The images were collected for different types of water. The dataset was manually created by labeling images. Findings: We evaluated the proposed approach’s results and discovered that changing the anchor size and increasing training iterations on the dataset reduced misclassification by 89.20%. Novelty: The proposed method improves accuracy by using suitable anchor boxes to distinguish the water body from the wet surface. Unlike existing systems that are only capable of detecting a particular type of water; the improved YOLO V3 detects wet surfaces and different types of stagnant water due to training on a real-time customized dataset.

Keywords: Object detection; Stagnant water; Street-View images; Misclassification; Mosquito breeding site


  1. Schenkel J, Taele P, Goldberg D, Horney J, Hammond T. Identifying Potential Mosquito Breeding Grounds: Assessing the Efficiency of UAV Technology in Public Health. Robotics. 2020;9(4):1–12. Available from: https://doi.org/10.3390/robotics9040091
  2. Passos WL, Araujo GM, Lima AAD, Netto SL, Silva EABD. Automatic detection of Aedes aegypti breeding grounds based on deep networks with spatio-temporal consistency. Computers, Environment and Urban Systems. 2022;93(101754). Available from: https://doi.org/10.48550/arXiv.2007.14863
  3. Wu X, Hong D, Chanussot J, Xu Y, Tao R, Wang Y. Fourier-Based Rotation-Invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection. IEEE Geoscience and Remote Sensing Letters. 2020;17(2):302–306. Available from: https://doi.org/10.1109/LGRS.2019.2919755
  4. Hong D, Gao L, Yao J, Zhang B, Plaza AJ, Chanussot J. Graph Convolutional Networks for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021;59(7):1–13. Available from: https://doi.org/10.1109/TGRS.2020.3015157
  5. Wu X, Hong D, Huang Z, Chanussot J. Infrared Small Object Detection Using Deep Interactive U-Net. IEEE Geoscience and Remote Sensing Letters. 2022;19:1–5. Available from: https://doi.org/10.1109/LGRS.2022.3218688
  6. Padmanabula SS, Puvvada RC, Sistla V, VKKK. Object Detection Using Stacked YOLOv3. Ingénierie des Systèmes d’Information . 2020;25(5):691–697. Available from: https://doi.org/10.18280/isi.250517
  7. Bhutad S, Patil K. Dataset of Stagnant Water and Wet Surface Label Images for Detection. Data in Brief. 2022;40(107752). Available from: https://doi.org/10.1016/j.dib.2021.107752
  8. Haddawy P, Wettayakorn P, Nonthaleerak B, Su Yin M, Wiratsudakul A, Schöning J, et al. Large scale detailed mapping of dengue vector breeding sites using street view images. PLOS Neglected Tropical Diseases. 2019;13(7):e0007555.
  9. Tong M, Xuyang J, Zheng Y, CW. A survey on machine learning for data fusion. Information Fusion. 2020;57:115–129. Available from: https://doi.org/10.1016/j.inffus.2019.12.001
  10. Alcorn MA, Li Q, Gong Z, Wang C, Mai L, Ku WS, et al. Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019.
  11. Gea J, Zhang A D, Yanga L. Road sludge detection and identification based on improved YOLO V3. 6th International Conference on Systems and Informatics. 2019;p. 1–5.
  12. Chen H, He Z, Shi B, Zhong T. Research on Recognition Method of Electrical Components Based on YOLO V3. IEEE Access. 2019;7:157818–157829.
  13. Xing C, Liang X, Ma Y. A Solution to Improve Object Detection for Images Captured by UAV-mounted Camera. 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). 2019. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8962431
  14. Minaee S, Boykov YY, Porikli F, Plaza AJ, Kehtarnavaz N, Terzopoulos D. Image Segmentation Using Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021;p. 1–20. Available from: https://doi.org/10.1109/TPAMI.2021.3059968
  15. Menghan Z, Zitian L, Yuncheng S. Optimization and Comparative Analysis of YOLOV3 Target Detection Method Based on Lightweight Network Structure. 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). 2020;p. 1–5. Available from: https;//doi.org/10.1109/ICAICA50127.2020.
  16. Huang YQ, Zheng JC. Optimized YOLOv3 Algorithm and Its Application in Traffic Flow Detections. Applied Sciences. 2020;10(9):3079. Available from: https://www.mdpi.com/2076-3417/10/9/3079


© 2023 Bhutad et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.