A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items

TL;DR

This paper introduces an incremental learning framework for instance segmentation of cluttered contraband items in baggage X-ray scans, effectively recognizing individual items without additional detectors and handling complex clutter.

Contribution

It proposes a novel encoder-decoder based incremental learning method that avoids catastrophic forgetting and improves instance segmentation in cluttered X-ray images.

Findings

Outperforms state-of-the-art methods on public datasets

Achieves better accuracy in cluttered scenarios

Balances detection accuracy and computational efficiency

Abstract

Screening cluttered and occluded contraband items from baggage X-ray scans is a cumbersome task even for the expert security staff. This paper presents a novel strategy that extends a conventional encoder-decoder architecture to perform instance-aware segmentation and extract merged instances of contraband items without using any additional sub-network or an object detector. The encoder-decoder network first performs conventional semantic segmentation and retrieves cluttered baggage items. The model then incrementally evolves during training to recognize individual instances using significantly reduced training batches. To avoid catastrophic forgetting, a novel objective function minimizes the network loss in each iteration by retaining the previously acquired knowledge while learning new class representations and resolving their complex structural inter-dependencies through Bayesian inference. A thorough evaluation of our framework on two publicly available X-ray datasets shows that it outperforms state-of-the-art methods, especially within the challenging cluttered scenarios, while achieving an optimal trade-off between detection accuracy and efficiency.