Broken Corn Detection Based on an Adjusted YOLO with Focal Loss

Abstract

Corns may be broken during corn mechanical harvesting. The ratio of broken corns measures the quality of mechanical harvesting and should be monitored in real time. This paper presents a method of detecting both broken and non-broken corns at the conveyor belt of a corn harvester based on the YOLO. The network structure of the YOLO is adjusted here to obtain more robust features so that it can work well in the open working space of the corn harvesting. Moreover, we improve the loss function to ensure that the hard examples can catch more attention during training. As it is difficult to obtain many training data of broken corns, the simulated broken corn images are generated from the real images of corns by a simple synthetic method. The concerned corn detection network of the proposed YOLO-based method is first trained with plenty of simulated samples and then fine-tuned with the real corn images. The experiments on real corn data confirm that the proposed YOLO-based method can achieve good accuracy and fast speed on the NVIDIA TX2.