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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.
PANDA : Perceptually Aware Neural Detection of Anomalies
Semi-supervised methods of anomaly detection have seen substantial advancement in recent years.
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.
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).
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