The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-M-2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-M-2-2022, 91–96, 2022
https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-91-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-M-2-2022, 91–96, 2022
https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-91-2022
 
25 Jul 2022
25 Jul 2022

MULTIPLE OIL PAD DETECTION USING DEEP LEARNING

A. Giri1, V. V. Sajith Variyar1, V. Sowmya1, R. Sivanpillai2, and K. P. Soman1 A. Giri et al.
  • 1Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
  • 2Wyoming GIS Center, University of Wyoming, Laramie, WY 82072, USA

Keywords: Deep Learning, Oil pads, Faster R-CNN, Aerial images, Data Augmentation

Abstract. Deep learning (DL) algorithms are widely used in object detection such as roads, vehicles, buildings, etc., in aerial images. However, the object detection task is still considered challenging for detecting complex structures, oil pads are one such example: due to its shape, orientation, and background reflection. A recent study used Faster Region-based Convolutional Neural Network (FR-CNN) to detect a single oil pad from the center of the image of size 256 × 256. However, for real-time applications, it is necessary to detect multiple oil pads from aerial images irrespective of their orientation. In this study, FR-CNN was trained to detect multiple oil pads. We cropped images from high spatial resolution images to train the model containing multiple oil pads. The network was trained for 100 epochs using 164 training images and tested with 50 images under 3 different categories. with images containing: single oil pad, multiple oil pad and no oil pad. The model performance was evaluated using standard metrics: precision, recall, F1-score. The final model trained for multiple oil pad detection achieved a weighted average for 50 images precision of 0.67, recall of 0.80, and f1 score of 0.73. The 0.80 recall score indicates that 80% of the oil pads were able to identify from the given test set. The presence of instances in test images like cleared areas, rock structures, and sand patterns having high visual similarity with the target resulted in a low precision score.