Multi-view object detection in crowded environments presents significant challenges, particularly for occlusion management across multiple camera views. This paper introduces a novel approach that extends conventional multi-view detection to operate directly within each camera’s image space. Our method finds objects bounding boxes for images from various perspectives without resorting to a bird’s eye view (BEV) representation. Thus, our approach removes the need for camera calibration by leveraging a learnable architecture that facilitates flexible transformations and improves feature fusion across perspectives to increase detection accuracy. Our model achieves Multi-Object Detection Accuracy (MODA) scores of 95.0% and 96.5% on the Wildtrack and MultiviewX datasets, respectively, significantly advancing the state of the art in multi-view detection. Furthermore, it demonstrates robust performance even without ground truth annotations, highlighting its resilience and practicality in real-world applications. These results emphasize the effectiveness of our calibration-free, multi-view object detector.

In this article, we present a multiagent framework for real-time large-scale 3-D reconstruction applications. In SLAM, researchers usually build and update a 3-D map after applying nonlinear pose graph optimization techniques. Moreover, many multiagent systems are prevalently using odometry information from additional sensors. These methods generally involve extensive computer vision algorithms and are tightly coupled with various sensors. We develop a generic method for the key challenging scenarios in multiagent 3-D mapping based on different camera systems. The proposed framework performs actively in terms of localizing each agent after the first loop closure between them. It is shown that the proposed system only uses monocular cameras to yield real-time multiagent large-scale localization and 3-D global mapping. Based on the initial matching, our system can calculate the optimal scale difference between multiple 3-D maps and then estimate an accurate relative pose transformation for large-scale global mapping.

The presence of any type of defect on the glass screen of smart devices has a great impact on their quality. We present a robust semi-supervised learning framework for intelligent micro-scaled localization and classification of defects on a 16K pixel image of smartphone glass. Our model features the efficient recognition and labeling of three types of defects: scratches, light leakage due to cracks, and pits. Our method also differentiates between the defects and light reflections due to dust particles and sensor regions, which are classified as non-defect areas. We use a partially labeled dataset to achieve high robustness and excellent classification of defect and non-defect areas as compared to principal components analysis (PCA), multi-resolution and information-fusion-based algorithms. In addition, we incorporated two classifiers at different stages of our inspection framework for labeling and refining the unlabeled defects. We successfully enhanced the inspection depth-limit up to 5 microns. The experimental results show that our method outperforms manual inspection in testing the quality of glass screen samples by identifying defects on samples that have been marked as good by human inspection.