Open AccessLemon‐YOLO: An efficient object detection method for lemons in the natural environment
Author(s) -
Li Guojin,
Huang Xiaojie,
Ai Jiaoyan,
Yi Zeren,
Xie Wei
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12171
Subject(s) - computer science , object detection , artificial intelligence , block (permutation group theory) , task (project management) , pattern recognition (psychology) , feature (linguistics) , feature extraction , set (abstract data type) , computer vision , backbone network , similarity (geometry) , reduction (mathematics) , image (mathematics) , mathematics , engineering , computer network , linguistics , philosophy , geometry , systems engineering , programming language
Abstract Efficient Intelligent detection is a key technology in automatic harvesting robots. However, citrus detection is still a challenging task because of varying illumination, random occlusion and colour similarity between fruits and leaves in natural conditions. In this paper, a detection method called Lemon‐YOLO (L‐YOLO) is proposed to improve the accuracy and real‐time performance of lemon detection in the natural environment. The SE_ResGNet34 network is designed to replace DarkNet53 network in YOLOv3 algorithm as a new backbone of feature extraction. It can enhance the propagation of features, and needs less parameter, which helps to achieve higher accuracy and speed. Moreover, the SE_ResNet module is added to the detection block, to improve the quality of representations produced from the network by strengthening the convolutional features of channels. The experimental results show that the proposed L‐YOLO has an average accuracy(AP) of 96.28% and a detection speed of 106 frames per second (FPS) on the lemon test set, which is 5.68% and 28 FPS higher than the YOLOv3, respectively. The results indicate that the L‐YOLO method has superior detection performance. It can recognize and locate lemons in the natural environment more efficiently, providing technical support for the machine's picking lemon and other fruits.