Object detection is a fundamental task in computer vision, which has been greatly progressed through developing large and intricate deep learning models. However, the lack of transparency is a big challenge that may not allow the widespread adoption of these models. Explainable artificial intelligence is a field of research where methods are developed to help users understand the behavior, decision logics, and vulnerabilities of AI-based systems. Black-box explanation refers to explaining decisions of an AI system without having access to its internals. In this paper, we design and implement a black-box explanation method named Black-box Object Detection Explanation by Masking (BODEM) through adopting a new masking approach for AI-based object detection systems. We propose local and distant masking to generate multiple versions of an input image. Local masks are used to disturb pixels within a target object to figure out how the object detector reacts to these changes, while distant masks are used to assess how the detection model's decisions are affected by disturbing pixels outside the object. A saliency map is then created by estimating the importance of pixels through measuring the difference between the detection output before and after masking. Finally, a heatmap is created that visualizes how important pixels within the input image are to the detected objects. The experimentations on various object detection datasets and models showed that BODEM can be effectively used to explain the behavior of object detectors and reveal their vulnerabilities. This makes BODEM suitable for explaining and validating AI based object detection systems in black-box software testing scenarios. Furthermore, we conducted data augmentation experiments that showed local masks produced by BODEM can be used for further training the object detectors and improve their detection accuracy and robustness.

We present a novel framework for instance segmentation of 3D buildings from Multi-view Stereo (MVS) urban scenes. Unlike existing works focusing on semantic segmentation of an urban scene, the emphasis of this work lies in detecting and segmenting 3D building instances even if they are attached and embedded in a large and imprecise 3D surface model. Multi-view RGB images are first enhanced to RGBH images by adding a heightmap and are segmented to obtain all roof instances using a fine-tuned 2D instance segmentation neural network. Roof instance masks from different multi-view images are then clustered into global masks. Our mask clustering accounts for spatial occlusion and overlapping, which can eliminate segmentation ambiguities among multi-view images. Based on these global masks, 3D roof instances are segmented out by mask back-projections and extended to the entire building instances through a Markov random field (MRF) optimization. Quantitative evaluations and ablation studies have shown the effectiveness of all major steps of the method. A dataset for the evaluation of instance segmentation of 3D building models is provided as well. To the best of our knowledge, it is the first dataset for 3D urban buildings on the instance segmentation level.