Oil Well Detection via Large-Scale and High-Resolution Remote Sensing Images Based on Improved YOLO v4

Abstract

Oil is an important resource for the development of modern society. Accurate detection of oil wells is of great significance to the investigation of oil exploitation status and the formulation of an exploitation plan. However, detecting small objects in large-scale and high-resolution remote sensing images, such as oil wells, is a challenging task due to the problems of large number, limited pixels, and complex background. In order to overcome this problem, first, we create our own oil well dataset to conduct experiments given the lack of a public dataset. Second, we provide a comparative assessment of two state-of-the-art object detection algorithms, SSD and YOLO v4, for oil well detection in our image dataset. The results show that both of them have good performance, but YOLO v4 has better accuracy in oil well detection because of its better feature extraction capability for small objects. In view of the fact that small objects are currently difficult to be detected in large-scale and high-resolution remote sensing images, this article proposes an improved algorithm based on YOLO v4 with sliding slices and discarding edges. The algorithm effectively solves the problems of repeated detection and inaccurate positioning of oil well detection in large-scale and high-resolution remote sensing images, and the accuracy of detection result increases considerably. In summary, this study investigates an appropriate algorithm for oil well detection, improves the algorithm, and achieves an excellent effect on a large-scale and high-resolution satellite image. It provides a new idea for small objects detection in large-scale and high-resolution remote sensing images.