Improvement of object detection in rice field environment with a fisheye-lens camera for robot combine based on a guided training method

Abstract

To achieve fully autonomous harvesting by robot combines, recognizing the external environments in rice fields is paramount. Two deep neural networks were used to detect the rice lodging, FCNResNet50 and FCNVGG16. In this study, a fisheye-lens camera was deployed to obtain a wider range of images. However, fisheye-lens cameras have a problem over the imaging frame, distortion. To resolve it, we propose to train models by adding cropped images from the specific area to the primitive dataset. By applying designated cropped images, these models achieved improved performance in testing original scenes. For FCNResNet50, the performance was 1–5 % better, while 10 % on average on FCNVGG16. Still, mean intersection over union (IoU), pixel accuracy and class accuracy also outperformed.