Structure overview of vegetation detection. A novel approach for efficient vegetation detection using an active lighting system

Fully autonomous navigation has been widely investigated for several decade of years; however, a safe and reliable navigation is still a daunting challenge in terrains containing vegetation. To improve the mobility capability of recent autonomous navigation systems, an additional vegetation detection function has been proposed. Since many proposals of generating vegetation classifier as well as suggestions of using different sensors to implement the function exist, a structured overview is required for vegetation detection in the context of outdoor navigation. Therefore, this paper studies and compares the accuracy and efficiency of existing vegetation detection approaches in a structured way. Furthermore, a new vision system set-up which combines CMOS sensor and Photo Mixer Device sensor with a near-infrared lighting system is also introduced to simultaneously provide depth, near-infrared and color images at high frame rate. Those near-infrared and color information are then used to compute vegetation index or train vegetation classifier to completely realize a real-time robust vegetation detection system. In this paper, a modification of the normalized difference vegetation index is devised, which is then defined as the new standard form of vegetation index for such vision system integrated with an additional lighting system. Finally, we will show the out-performance of the proposed approach in comparison with more conventional ones.

► Structural overview of vegetation detection.
► Introducing a new vision set-up using an active lighting system.
► Modification of normalized difference vegetation index is devised.
► Proposed approach enables robust and real-time vegetation detection.
► A quantitative and qualitative comparison of available methods are described.