Detection Method of Fat Content in Pig B-Ultrasound Based on Deep Learning

• Published in: IEICE Transactions on Information and Systems Vol. E106.D; no. 5; pp. 726 - 734
• Main Authors: DONG, Wenxin, ZHANG, Jianxun, TAN, Shuqiu, ZHANG, Xinyue
• Format: Journal Article
• Language: English
• Published: Tokyo The Institute of Electronics, Information and Communication Engineers 01.05.2023; Japan Science and Technology Agency
• Subjects: Animal fat; B-ultrasound image; convolutional neural network; Deep learning; fat content detection; nondestructive testing; Pork; Semantics; Swine; Ultrasonic imaging
• ISSN: 0916-8532; 1745-1361
• DOI: 10.1587/transinf.2022DLP0022

In the pork fat content detection task, traditional physical or chemical methods are strongly destructive, have substantial technical requirements and cannot achieve nondestructive detection without slaughtering. To solve these problems, we propose a novel, convenient and economical method for detecting the fat content of pig B-ultrasound images based on hybrid attention and multiscale fusion learning, which extracts and fuses shallow detail information and deep semantic information at multiple scales. First, a deep learning network is constructed to learn the salient features of fat images through a hybrid attention mechanism. Then, the information describing pork fat is extracted at multiple scales, and the detailed information expressed in the shallow layer and the semantic information expressed in the deep layer are fused later. Finally, a deep convolution network is used to predict the fat content compared with the real label. The experimental results show that the determination coefficient is greater than 0.95 on the 130 groups of pork B-ultrasound image data sets, which is 2.90, 6.10 and 5.13 percentage points higher than that of VGGNet, ResNet and DenseNet, respectively. It indicats that the model could effectively identify the B-ultrasound image of pigs and predict the fat content with high accuracy.