TY - GEN
T1 - BALANCED AFFINITY LOSS FOR HIGHLY IMBALANCED BAGGAGE THREAT CONTOUR-DRIVEN INSTANCE SEGMENTATION
AU - Ahmed, Abdelfatah
AU - Obeid, Ahmad
AU - Velayudhan, Divya
AU - Hassan, Taimur
AU - Damiani, Ernesto
AU - Werghi, Naoufel
N1 - Funding Information:
This work is supported by a research fund from Khalifa University. Ref: CIRA-2019-047 and the Abu Dhabi Department of Education and Knowledge (ADEK), Ref: AARE19-156.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Autonomous detection of threat items from baggage X-ray imagery is one of the most vital and challenging tasks. Manual detection of these items is a cumbersome, slow, and error-ridden process which is also limited by the examination capacity of the security inspector. To overcome these limitations, many researchers have proposed deep learning-driven approaches to recognize suspicious objects from the baggage X-ray scans. However, threat items are rarely seen in the real world compared to innocuous baggage content. Therefore, when trained with imbalanced data, the performance of the conventional threat detection models drastically decreases. This paper addresses these issues with a contour-driven instance segmentation model optimized with a novel combined loss function, dubbed balanced affinity loss function. In addition to mitigating the class imbalance, this function best handles the fine-grained classification aspect inferred by contours and the instance segmentation. We validated the proposed system on three public baggage X-ray datasets, where it outperformed state-of-the-art methods by 7.76%, 25.81%, and 8.78% in terms of intersection-over-union score.
AB - Autonomous detection of threat items from baggage X-ray imagery is one of the most vital and challenging tasks. Manual detection of these items is a cumbersome, slow, and error-ridden process which is also limited by the examination capacity of the security inspector. To overcome these limitations, many researchers have proposed deep learning-driven approaches to recognize suspicious objects from the baggage X-ray scans. However, threat items are rarely seen in the real world compared to innocuous baggage content. Therefore, when trained with imbalanced data, the performance of the conventional threat detection models drastically decreases. This paper addresses these issues with a contour-driven instance segmentation model optimized with a novel combined loss function, dubbed balanced affinity loss function. In addition to mitigating the class imbalance, this function best handles the fine-grained classification aspect inferred by contours and the instance segmentation. We validated the proposed system on three public baggage X-ray datasets, where it outperformed state-of-the-art methods by 7.76%, 25.81%, and 8.78% in terms of intersection-over-union score.
KW - Affinity Loss
KW - Baggage X-ray Imagery
KW - Contour Instance Segmentation
KW - Imbalanced Threat Detection
UR - https://www.scopus.com/pages/publications/85139208053
U2 - 10.1109/ICIP46576.2022.9897490
DO - 10.1109/ICIP46576.2022.9897490
M3 - Conference contribution
AN - SCOPUS:85139208053
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 981
EP - 985
BT - 2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PB - IEEE Computer Society
T2 - 29th IEEE International Conference on Image Processing, ICIP 2022
Y2 - 16 October 2022 through 19 October 2022
ER -