An Optimized Mask R-CNN with Bag-of-Visual Words and Fast+Surf Algorithm in Sharp Object Instance Segmentation for X-ray Security

Abstract

Automated security X-ray analysis is highly desired for efficiently inspecting sharp objects. The research formulated an optimized approach for sharp object detection using a Mask R-CNN architecture. The dataset used during the training phase consists of 238 balanced raw images extracted from GitHub named OPIXray. The researchers utilized recent advances in computer vision algorithms, including the Bag-of-Words and Fast+Surf feature extraction techniques, to improve the accuracy and reliability of object deletion. The research demonstrated that the optimized versions of the classification and object detection models have significantly improved accuracy for most categories, with a 5% improvement for the clear category and a 3% improvement for both the scissor and straight knife detection.