Journal Article

Improving YOLOv5 with Attention Mechanism for Detecting Boulders from Planetary Images

Geng, Xun, Zhu, Linlin, Li, Zheng, Liu, Chun

2021

Overview

It is of great significance to apply the object detection methods to automatically detect boulders from planetary images and analyze their distribution. This contributes to the selection of candidate landing sites and the understanding of the geological processes. This paper improves the state-of-the-art object detection method of YOLOv5 with attention mechanism and designs a pyramid based approach to detect boulders from planetary images. A new feature fusion layer has been designed to capture more shallow features of the small boulders. The attention modules implemented by combining the convolutional block attention module (CBAM) and efficient channel attention network (ECA-Net) are also added into YOLOv5 to highlight the information that contribute to boulder detection. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset which is widely used for object detection evaluations and the boulder dataset that we constructed from the images of Bennu asteroid, the evaluation results have shown that the improvements have increased the performance of YOLOv5 by 3.4% in precision. With the improved YOLOv5 detection method, the pyramid based approach extracts several layers of images with different resolutions from the large planetary images and detects boulders of different scales from different layers. We have also applied the proposed approach to detect the boulders on Bennu asteroid. The distribution of the boulders on Bennu asteroid has been analyzed and presented.

• Publisher: MDPI AG
• Subject: Accuracy / Apollo asteroids / Asteroids / attention mechanism / Bennu asteroid / boulder detection / boulder distribution / Boulders / data collection / Datasets / Deep learning / design / Geological processes / image analysis / land / Landing sites / Modules / Object recognition / planetary exploration / remote sensing / YOLOv5