An Occlusion-Representation and Background-Redundancy-Suppression Network for Prohibited Item Detection with X-Ray Images.

Hao Wen, Yanxi Zhang, Yanzu Huang, Hongji Xie, Xiangdong Gao

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

5 authors.

Hao WenSchool of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510060, China.

Yanxi ZhangSchool of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510060, China.ORCID 0000-0002-1378-5391

Yanzu HuangSchool of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510060, China.

Hongji XieSchool of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510060, China.

Xiangdong GaoSchool of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510060, China.

Funding

Guangdong Natural Science Foundation 2025A1515010910Open Project of Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection 2023B1212010011

6 · The paper itself

Abstract

Prohibited item detection in baggage X-ray images remains challenging because object overlap, partial occlusion, and cluttered backgrounds often obscure target boundaries. These factors make discriminative structural cues difficult to preserve in complex luggage scenes. This study proposes YOLO11-ORS, an occlusion representation and background redundancy suppression network built upon YOLO11n. The proposed model improves feature representation in two ways. First, a Background Redundancy Suppression Pyramid (BRSP) module is designed after semantic feature aggregation to refine contextual features at multiple scales and suppress irrelevant responses from cluttered baggage contents. Second, an Occluded Structure Enhancement Module (OSEM) is constructed at the attention-based feature transformation stage to preserve weak edge cues and incomplete local structures of partially occluded prohibited items. Experiments on the SIXray dataset show that YOLO11-ORS achieves 94.1%

Indexed as

background redundancy suppressionocclusion representationprohibited item detectionX-ray security imagesYOLO11-ORS

Identifiers

PMID42740189

DOI10.3390/s26175569

PMCPMC13568384

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.