Detection and Classification of Vegetation for Roadside Vegetation Inspection and Rehabilitation Using Deep Learning Techniques

Roadside vegetation plays a critical role in the safety and aesthetics of highways and roadways. Well-maintained roadside vegetation aids in soil conservation, improves drainage, stabilizes the slopes, reduces runoff, and improves travel safety. A routine inspection of roadside vegetation is a key requisite for a successful roadside vegetation management system. However, on a regional scale (e.g., county level), the manual routine inspection of roadside vegetation would be extremely labor-intensive and expensive. Therefore, an automated vegetation inspection system is required for the detection and condition assessment of roadside vegetation. To this end, the main objective of this study is to develop an automated inspection system using the state-of-the-art deep learning model to detect roadside vegetation from aerial photography images and classify them based on their quality for maintenance and rehabilitation. The proposed system utilizes the U-Net model to train on a publicly available high-resolution aerial imagery data set from Central Texas and test on images from Texas highways. The results show promising accuracy and precision for roadside vegetation detection and classification. It is expected that the proposed framework will aid transportation agencies in the inspection of roadside vegetation, thereby facilitating proactive rehabilitation decisions.