Locating Wood Pith in a Wood Stem Cross Sectional Image Using YOLO Object Detection Sawmills rely on locating wood pith at the end surface of a wood stem either to automate the process and maximize the yield or to perform wood quality assessment of wood planks. Previously, wood pith location was approximated by applying image processing algorithms along with some geometric relations between the annual rings within a crosssectional image of wood and its pith. This produces failed detection results if the wood stems deviate from a circular shape. In this paper, we propose employing YOLO (you only look once) object detection to a wood pith identification problem. The TinyYOLO network with 23 layers was trained using a transfer learning approach with 345 wood crosssectional images with pith annotations. Different experiments with variable ratios between the training and testing datasets and training iterations were carried out to produce checkpoints and test against the testing datasets. We found that the minimum loss function which is commonly used to indicate the best trained model in other deep neural networks (DNNs) object detection is not effective for this application. Therefore, we used detection accuracy, to reflect the percentage of detectable piths while inferencing the trained model with its testing dataset and an average distance error, i.e., the average distances between the detected piths and their ground truths, as the accuracy measurement. The experimental result shows that a ratio of training and testing datasets of 70:30, at training iterations of 170,000, gives rise to the maximum detection value of 76.3% with a 16.6pixel average distance error. Additionally, it produces only 25% average distance error; frac10344176 with 50% standard deviation; frac25335395, compared to the state of the art nonDNNbased pith detection approach.

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.