Seeing the Trees from the Forest: Using Modern Methods to Identify Individual Objects in a Cluttered Environment for Robots

Robotics and computer vision are areas of high growth across both industry and personal usage environments. Robots in industrial situations have been used to work in environments that are hazardous for humans or to perform basic tasks that require fine detail beyond that which human operators can reliably perform. These robotic solutions require a variety of sensors and cameras to navigate and identify objects within their working environment, as well as software and intelligent detection systems. These solutions generally require high definition depth cameras, laser range finders and computer vision algorithms, which are both expensive and require expensive graphics processors to run practically. This thesis explores the option of a low-cost computer vision enabled robotic solution, which can operate within a forestry environment. Starting with the accuracy of camera technologies, testing two of the main cameras available for robotic vision, and demonstrating the benefits of the RealSense D435 by Intel over the Kinect for X-Box One. Followed by testing common object detection and recognition algorithms on different devices; considering the advantages and weaknesses of the determined models for the intended purpose of forestry. These tests support other research on finding that the MobileNet Single Shot Detector has the fastest recognition speeds with accurate precision, however, it struggles where multiple objects were present, or the background was complex. In comparison, the Mask R-CNN had high accuracy and was able to identify objects consistently even with large numbers overlaid within a single frame. A combined method based on the Faster R-CNN architecture with a MobileNet backbone and masking layers is proposed, developed and tested based on these findings. This method utilized the feature extraction and object detection abilities of the faster MobileNet in place of the traditionally ResNet based feature proposal networks, while still capitalizing on the benefits of the region of interest (ROI) align and masking from the Mask R-CNN architecture. The results from this model did not meet the criteria required to recommend the model as an operational solution for the forestry environment. However, they do show that the model has higher performance and average precision than other models with similar frame rates on the non-CUDA enabled testing device. Demonstrating the technology and methodology has the potential to be the basis for a future solution to the problem of balancing accuracy and performance on a low performance or non GPU-enabled robotic unit.