Fast location and classification of small targets using region segmentation and a convolutional neural network Abstract   The hickory nut has rich nutritional and high economic value, but its inner wall structure is complex, making it inconvenient to separate the kernel from the hickory nut fragments for consumption. In this study, we apply a deep convolutional neural network algorithm to hickory nut fragments after shell breaking, to accurately sort kernels, shells, and unseparated bodies. Region segmentation is used to eliminate interference when locating multiple small targets of different sizes, and a custom dataset of 15,000 target images after shell breaking is created. Then, three different loss function optimization methods are evaluated for model optimization. Results revealed that the model only minimizes the average crossentropy loss, and that using dropout at the first fully connected layer with a dropout rate 0.1 provides the best shell, kernel, and unseparated body identification accuracy of 97.14%, 99.66%, and 96.48%, respectively. Notably, the kernel and unseparated body recognition rates are considerably improved over other methods. Finally, a total of 450 hickory nut fragments were divided into 30 batches for a sorting experiment. The resulting shell, kernel, and unseparated body identification accuracy is 94.00%, 98.67%, and 95.33%, respectively. These results demonstrate that the proposed method provides excellent classification of hickory nut fragments according to the material. This approach can be easily applied to any small target sorting application.

Semantic Segmentation of Sheep Organs by Convolutional Neural Networks The recognition of individual organs by machine vision is an important step towards the processing of lamb meat and the extraction of edible offal. This paper presents the first semantic segmentation method to localise and label sheep organs in images of the extracted organ package. The challenges around the semantic segmentation of lamb organs include the lack of available data sets, the variable orientation, shape and appearance of organs, and the wet and specular surface. To address these challenges, we created a new dataset for sheep organ segmentation consisting of images and pixelwise labels for seven types of organs and tissues. To produce baseline results on the data set, a convolutional neural network is trained to partition images into segments corresponding to individual organs.