Analyzing the Potential of ReXNet-150: A Novel Architecture for Automobile Parts Classification

Excellent automotive inspection is one of the key application areas for getting enhanced quality at a low cost, and it may be obtained using advanced computer vision technology. Automated car component assembly and quality control depend on autonomous automotive part recognition. ReXNet’s efficient and lightweight architecture enables the automation of auto component assembly and the improvement of quality control processes with an advanced solution. With the help of ReXNet, autonomous cars can effectively recognize and inspect a wide range of parts, including electrical modules, actuators, connectors, and sensors. The dataset comprises 16,600 RGB photos depicting 50 distinct types of auto parts. It encompasses a train set, test set, and validation set. Each of the 50 classes in both the validation and test sets has five images. In the identification of individual parts, the maximum testing accuracy of 98.6% and the maximum training accuracy of 98.4% were reached. Efficient car inspection systems, model verification, and vehicle manufacturing are just a few of the areas that stand to benefit significantly from such precise car part classification. Classifying car parts is helpful for several purposes, including model verification, automotive inspecting systems, and automobile manufacturing.