Affiliations: School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
Correspondence:
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Corresponding author. Kaiqiong Sun, School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China.
E-mail: kaiqiong@163.com.
Abstract: While deep learning based object detection methods have achieved high accuracy in fruit detection, they rely on large labeled datasets to train the model and assume that the training and test samples come from the same domain. This paper proposes a cross-domain fruit detection method with image and feature alignments. It first converts the source domain image into the target domain through an attention-guided generative adversarial network to achieve the image-level alignment. Then, the knowledge distillation with mean teacher model is fused in the yolov5 network to achieve the feature alignment between the source and target domains. A contextual aggregation module similar to a self-attention mechanism is added to the detection network to improve the cross-domain feature learning by learning global features. A source domain (orange) and two target domain (tomato and apple) datasets are used for the evaluation of the proposed method. The recognition accuracy on the tomato and apple datasets are 87.2% and 89.9%, respectively, with an improvement of 10.3% and 2.4%, respectively, compared to existing methods on the same datasets.
Keywords: Domain adaptation, deep learning, knowledge distillation, fruit detection
