A real‐time classification and detection method for mutton parts based on single shot multi‐box detector. (17th May 2021)

• Record Type: Journal Article
• Authors: Zhao, Shida; Hao, Guangzhao; Zhang, Yichi; Wang, Shucai
• Is Part Of: Journal of food process engineering. Volume 44:Number 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-17
• Subjects: Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; 664.005
• DOI: 10.1111/jfpe.13749
• Languages: English
• ISSNs: 0145-8876

Abstract

Abstract: This article proposes a real‐time classification and detection method for mutton parts based on a single shot detector (SSD). We acquired 9, 000 images of various parts of mutton in a sheep slaughtering workshop, characterized by multiple classes and multiple samples. After image preprocessing, an image dataset of the mutton parts was established for later model training. Subsequently, we introduced transfer learning to train an SSD–VGG network and obtain the optimal model. The optimal model was then applied to determine the category and position of each mutton part in the image, thus realizing the classification and detection of mutton parts. In this method, the average accuracy mAP and average processing time for a single image are selected as the accuracy and speed indicators, respectively, for judging the detection performance of the model. The feature extraction network VGG is replaced with MobileNetV1 to optimize the real‐time performance of the SSD. Furthermore, we set an additional illumination dataset with two brightness levels "bright" and "dark" to verify the generalization ability of the optimized model. Finally, four common object detection algorithms, namely YoloV3–MobileNetV1, YoloV3–DarkNet53, Fast‐RCNN, and Cascade‐RCNN, are introduced to perform comparative experiments on mutton image datasets. The test results prove that the SSD–MobileNetV1 exhibits high accuracy and good real‐time performance, with a certain generalization ability. It has a better comprehensive detection ability than other methods and can provide technical support for mutton processing. Practical Applications: Currently, in the processing of mutton, the multiple parts of mutton are identified and sorted manually, which is time‐consuming and laborious, and there are certain hidden food safety hazards. A deep‐learning‐based object detection method can solve the above problems effectively. Therefore, this study uses SSD to perform an accurate real‐time recognition of the multiple parts of mutton from its images and provide a visual guidance for mutton sorting robots. It can also aid further research in the slaughtering and processing of other meat. Abstract : This study used six types of mutton parts as the research objects, based on the SSD to accurately obtain the categories of various types of mutton parts. Subsequently, the detection speed of SSD model was optimized by changing the feature extraction network to MobileNetV1, and the optimized model's generalization ability of the mutton parts under different illumination is verified. Further comparative experiments with various methods showed that the optimized model had the best comprehensive detection ability to a variety of mutton parts.