A Sequential CNN Approach for Foreign Object Detection in Hyperspectral Images

This paper reports about potentials of hyperspectral imaging for object detection, especially on an application of foreign object detection (FOD) in meat products. A sequential deep-learning framework is proposed by using region-proposal networks (RPNs) and 3D convolutional networks (CNNs). Two independent datasets of images, contaminated with many types of foreign materials, were used for training and testing the proposed model. Results show that the proposed RPN model outperforms a selected search method in terms of accuracy, efficiency, and run-time. An FOD model based on RPN and 3D-CNN, or selected search with a 3D-CNN solve FOD with an average precision of 81.0% or 50.6%, respectively. This study demonstrates opportunities when using hyperspectral imaging systems for real-time object detection by using both spectral and spatial features combined.