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Found 18 papers, 2 papers with code
Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery
Our results show that SAM can segment objects in the X-ray modality when given a box prompt, but its performance varies for point prompts.
Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers
Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment.
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption
Anomaly detection is the task of recognising novel samples which deviate significantly from pre-establishednormality.
Detecting Anomalies using Generative Adversarial Networks on Images
Most of the available data in the anomaly detection task is imbalanced as the number of positive/anomalous instances is sparse.
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.
Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery
X-ray baggage security screening is in widespread use and crucial to maintaining transport security for threat/anomaly detection tasks.
Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery
When training and evaluating on uncompressed data as a baseline, we achieve maximal mean Average Precision (mAP) of 0. 823 with Cascade R-CNN across the FLIR dataset, outperforming prior work.
Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery
Within commercial wind energy generation, the monitoring and predictive maintenance of wind turbine blades in-situ is a crucial task, for which remote monitoring via aerial survey from an Unmanned Aerial Vehicle (UAV) is commonplace.
Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery
The recent advancement in deep Convolutional Neural Network (CNN) has brought insight into the automation of X-ray security screening for aviation security and beyond.
On the impact of using X-ray energy response imagery for object detection via Convolutional Neural Networks
Automatic detection of prohibited items within complex and cluttered X-ray security imagery is essential to maintaining transport security, where prior work on automatic prohibited item detection focus primarily on pseudo-colour (rgb}) X-ray imagery.
Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection
Automatic visual fire detection is used to complement traditional fire detection sensor systems (smoke/heat).
Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery
X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on automatic prohibited item detection focus primarily on 2D X-ray imagery.
On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery
As the first attempt to use 3D CNN for volumetric 3D CT baggage security screening, we first evaluate different CNN architectures on the classification of isolated prohibited item volumes and compare against traditional methods which use hand-crafted features.
Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection
In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery.
Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery
X-ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items.
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery
X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles.
The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composited X-ray Imagery
This work opens up the possibility of using synthetically composed imagery, avoiding the need to collate such large volumes of hand-annotated real-world imagery.
Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery
Subsequently, leveraging a range of established CNN object and fine-grained category classification approaches we formulate within object anomaly detection as a two-class problem (anomalous or benign).
