Welcome to the InfoSci Platform
FPN-Based Small Orange Fruit Detection From Farm Images With Occlusion

FPN-Based Small Orange Fruit Detection From Farm Images With Occlusion

Francisco de Castro, Angelin Gladston
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 12
ISSN: 2155-6393|EISSN: 2155-6407|EISBN13: 9781683182047|DOI: 10.4018/IJKBO.296394
Cite Article Cite Article

MLA

de Castro, Francisco, and Angelin Gladston. "FPN-Based Small Orange Fruit Detection From Farm Images With Occlusion." IJKBO vol.12, no.1 2022: pp.1-12. http://doi.org/10.4018/IJKBO.296394

APA

de Castro, F. & Gladston, A. (2022). FPN-Based Small Orange Fruit Detection From Farm Images With Occlusion. International Journal of Knowledge-Based Organizations (IJKBO), 12(1), 1-12. http://doi.org/10.4018/IJKBO.296394

Chicago

de Castro, Francisco, and Angelin Gladston. "FPN-Based Small Orange Fruit Detection From Farm Images With Occlusion," International Journal of Knowledge-Based Organizations (IJKBO) 12, no.1: 1-12. http://doi.org/10.4018/IJKBO.296394

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Fruit detection using deep learning is yielding very good performance, the goal of this work is to detect small fruits in images under these occlusion and overlapping conditions. The overlap among fruits and their occlusion can lead to false and missing detection, which decreases the accuracy and generalization ability of the model. Therefore, a small orange fruit recognition method based on improved Feature Pyramid Network was developed. To begin with, multi-scale feature fusion was used to fuse the detailed bottom features and high-level semantic features to detect small-sized orange to improve recognition rate. And then repulsion loss was used to take place of the original smooth L1 loss function. Besides, Soft non-maximum suppression was adopted to replace non-maximum suppression to screen the bounding boxes of orange to construct a recognition model of orange fruits. Finally, the network was trained and verified on the collected image data set. The results showed that compared with the traditional detection models, the mean average precision was improved from 79.7 to 82.8%.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.