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A Deep Learning-Based Object Detection Framework For Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images, Ibrahim Hassan Syed, Susan Mckeever Dr., Kieran Feighan, David Power, Dympna O'Sullivan
A Deep Learning-Based Object Detection Framework For Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images, Ibrahim Hassan Syed, Susan Mckeever Dr., Kieran Feighan, David Power, Dympna O'Sullivan
Conference papers
Road maintenance and the early detection of road defects rely on routine pavement inspections. While advanced 3D laser profiling systems have the capability to automatically identify certain types of distress such as cracks and ruts, more complex pavement damage, including patches, often require manual identification. To address this limitation, this study proposes an automated patch detection system that employs object detection techniques. The results demonstrate the ability of object detection models to accurately identify patches in laser profiling images, indicating that the proposed approach has the capability to significantly enhance automation in visual inspection processes. This has the potential for …
Wifi-Based Human Activity Recognition Using Attention-Based Bilstm, Amany Elkelany, Robert J. Ross, Susan Mckeever
Wifi-Based Human Activity Recognition Using Attention-Based Bilstm, Amany Elkelany, Robert J. Ross, Susan Mckeever
Conference papers
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the …
Detecting Patches On Road Pavement Images Acquired With 3d Laser Sensors Using Object Detection And Deep Learning, Ibrahim Hassan Syed, Dympna O'Sullivan, Susan Mckeever, David Power, Ray Mcgowan, Kieran Feighan
Detecting Patches On Road Pavement Images Acquired With 3d Laser Sensors Using Object Detection And Deep Learning, Ibrahim Hassan Syed, Dympna O'Sullivan, Susan Mckeever, David Power, Ray Mcgowan, Kieran Feighan
Academic Posters Collection
Regular pavement inspections are key to good road maintenance and road defect corrections. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of different defects using 3D lasers. However, such systems still require manual involvement to complete the detection of pavement defects. This work proposes an automatic patch detection system using an object detection technique. Results show that the object detection model can successfully detect patches inside LCMS images and suggest that the proposed approach could be integrated into the existing pavement inspection systems.
Explaining Deep Learning Time Series Classification Models Using A Decision Tree, Ephrem T. Mekonnen, Pierpaolo Dondio, Luca Longo
Explaining Deep Learning Time Series Classification Models Using A Decision Tree, Ephrem T. Mekonnen, Pierpaolo Dondio, Luca Longo
Academic Posters Collection
This preliminary study proposes a new post hoc method to explain deep learning-based time series classification models using a decision tree. Our approach generates a decision tree graph or rulesets as an explanation, improving interpretability compared to saliency map-based methods. The method involves two phases: training and evaluating the deep learning-based time series classification model and extracting prototypical events from the evaluation set to train the decision tree classifier. We conducted experiments on artificial and real datasets, evaluating the explanations based on accuracy, fidelity, number of nodes, and depth. Our preliminary findings suggest that our post-hoc method improves the interpretability …