Improved YOLOv8 Detection Algorithm in Security Inspection Image
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Abstract (arXiv)
Security inspection is the first line of defense to ensure the safety of people's lives and property, and intelligent security inspection is an inevitable trend in the future development of the security inspection industry. Aiming at the problems of overlapping detection objects, false detection of contraband, and missed detection in the process of X-ray image detection, an improved X-ray contraband detection algorithm CSS-YOLO based on YOLOv8s is proposed.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 12 Aug 2023.
Builds on
10 of 21 references.
References · 10
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2017Mask R-CNNcited by 7,259
- 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
- 2015SSD: Single Shot MultiBox Detectorcited by 3,125
- 2018CBAM: Convolutional Block Attention Modulecited by 2,463
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
- 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
- 2017Soft-NMS -- Improving Object Detection With One Line of Codecited by 380
- 2019SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Imagescited by 52
- 2020Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Modulecited by 39
Built on by
Citing papers per year: 2023: 1; 2024: 1.
2 papers.