Non-Local Means 잡음 제거와 데이터 증강을 이용한 YOLO 기반 객체 특징 탐색

Abstract

When fruits are harvested in farms, most of them go through a manual sorting process and classify and distribute decomposed fruits. However, there is a limit to manually classifying large amounts in a situation where the number of workers is decreasing in farms. To solve this problem, it is important to divide normal and decomposed fruits in real time to minimize the proportion of manpower used in the screening process. We propose a method of YOLO based Object Features Detection Using Non-Local Means Denoising and Data Augmentation. The proposed method collects desired data through the Crawling method, and the preprocessing minimizes image noise through Non-Local Means Denoising. The built image dataset uses YOLOv3, an object detection algorithm, to distinguish and detect normal and decayed fruits. As a result of performance evaluation, object detection of YOLOv3 objects in a proposed method rather than detection results shows that Recall increases 10% performance and increases 9% in the remaining Recall and IoU. Therefore, the proposed method can increase screening efficiency by detecting decayed fruits well.