Image-Based Incremental Learning for Part Recognition of Used Automotive Cores in Reverse Logistics

This paper presents a study on image-based incremental learning for part recognition of used automotive parts, also known as cores. The use of Machine Learning (ML) in the recognition of used parts has proven to be effective in suggesting Original Equipment Number (OEN) based on images and logistics data of a core. This leads to a four-eye process where the worker and ML interact through an assistance system. In reverse logistics, the spectrum of parts handled is constantly changing, making it difficult to have a "complete" image or sensor-based data set. The study focuses on the ramp-up phase of an ML implementation project in a real-world automotive core sorting station. There are two stations equipped with sensors such as RGB cameras. The sorted parts were acquired over a period of one year. Incremental learning was employed to cope with the growing dataset and the growing number of classes to be identified without retraining a model from scratch. Open source and state-of-the-art incremental ML learning methods such as POD-Net and Foster were tested against the common joint training approach used for most benchmarks in computer vision. The best-fitting open-source method for this problem was identified as POD-Net used with a self-supervised pretrained ResNet50. For the ramp-up of an ML-based core recognition a combination of incremental learning and joint training was found to be useful. It starts learning from a small number of digitized parts (14 classes), while maintaining a high recognition accuracy rate throughout the year, with a final class count of 100 (an increase of approx. 600%), which is a subset of a real application problem. The results of this study show that the proposed method is efficient, plastic, and energy-saving. Thus, it is a promising approach for the recognition of used automotive parts.