FAR-Net: Feature-Wise Attention-Based Relation Network for Multilabel Jujube Defect Classification

Abstract

In production, due to natural conditions or process peculiarities, a single product often may exhibit more than one type of defect. The accurate identification of all defects has an important guiding significance and practical value to improve the planting and production processes. Concerning the surface defect classification task, convolutional neural networks can be implemented as a powerful instrument. However, a typical convolutional neural network tends to consider an image as an inseparable entity and a single instance when extracting features; moreover, it may overlook semantic correlations between different labels. To address these limitations, in the present paper, we proposed a feature-wise attention-based relation network (FAR-Net) for multilabel jujube defect classification. The network included four different modules designed for (1) image feature extraction, (2) label-wise feature aggregation, (3) feature activation and deactivation, and (4) correlation learning among labels. To evaluate the proposed method, a unique multilabel jujube defect dataset was constructed as a benchmark for the multilabel classification task of the jujube defect images. The results of experiments show that owing to the relation learning mechanism, the average precision of the three main composite defects in the dataset increases by 5.77%, 4.07%, and 3.50%, respectively, compared to the backbone of our network, namely Inception v3, which indicated that the proposed FAR-Net effectively facilitated the learning of correlation between labels and eventually, improved the multilabel classification accuracy.

Detection and Classification of Defective Hard Candies Based on Image Processing and Convolutional Neural Networks

“…Arthur et al trained the deep residual neural network (ResNet) classifier to detect the external defects on tomatoes, and they found that fine-tuning outperformed feature extraction, revealing the benefit of training additional layers when sufficient data samples are available [16]. Xu et al proposed a feature-wise attention-based relation network (FAR-Net) for multilabel jujube defect classification, which effectively facilitated the learning of correlation between labels and improved the multilabel classification accuracy [17]. Ahmad et al used an improved CNN algorithm to detect the apparent defects of sour lemon fruit and graded them [18].…”