Bfp net: balanced feature pyramid network for small apple detection in complex orchard environment

Sun, Meili, Xu, Liancheng, Chen, Xiude, Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902, Zheng, Yuanjie and Jia, Weikuan 2022. Bfp net: balanced feature pyramid network for small apple detection in complex orchard environment. Plant Phenomics 2022 , 9892464. 10.34133/2022/9892464

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Abstract

Despite of significant achievements made in the detection of target fruits, small fruit detection remains a great challenge, especially for immature small green fruits with a few pixels. The closeness of color between the fruit skin and the background greatly increases the difficulty of locating small target fruits in the natural orchard environment. In this paper, we propose a balanced feature pyramid network (BFP Net) for small apple detection. This network can balance information mapped to small apples from two perspectives: multiple-scale fruits on the different layers of FPN and a characteristic of a new extended feature from the output of ResNet50 conv1. Specifically, we design a weight-like feature fusion architecture on the lateral connection and top-down structure to alleviate the small-scale information imbalance on the different layers of FPN. Moreover, a new extended layer from ResNet50 conv1 is embedded into the lowest layer of standard FPN, and a decoupled-aggregated module is devised on this new extended layer of FPN to complement spatial location information and relieve the problem of locating small apple. In addition, a feature Kullback-Leibler distillation loss is introduced to transfer favorable knowledge from the teacher model to the student model. Experimental results show that of our method reaches 47.0%, 42.2%, and 35.6% on the benchmark of the GreenApple, MinneApple, and Pascal VOC, respectively. Overall, our method is not only slightly better than some state-of-the-art methods but also has a good generalization performance.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	American Association for the Advancement of Science
ISSN:	2643-6515
Date of First Compliant Deposit:	31 October 2022
Date of Acceptance:	31 August 2022
Last Modified:	31 Oct 2022 10:30
URI:	https://orca.cardiff.ac.uk/id/eprint/153846

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