Yijiashun Qi

Tianqi, Ding, Dawei Xiang et al.

The rapid growth of industrial automation has highlighted the need for precise and efficient defect detection in large-scale machinery. Traditional inspection techniques, involving manual procedures such as scaling tall structures for visual evaluation, are labor-intensive, subjective, and often hazardous. To overcome these challenges, this paper introduces an automated defect detection framework built on Neural Radiance Fields (NeRF) and the concept of digital twins. The system utilizes UAVs to capture images and reconstruct 3D models of machinery, producing both a standard reference model and a current-state model for comparison. Alignment of the models is achieved through the Iterative Closest Point (ICP) algorithm, enabling precise point cloud analysis to detect deviations that signify potential defects. By eliminating manual inspection, this method improves accuracy, enhances operational safety, and offers a scalable solution for defect detection. The proposed approach demonstrates great promise for reliable and efficient industrial applications.

Tianyao Sun, Dawei Xiang, Tianqi Ding et al.

Infrared and visible image fusion (IVIF) is a fundamental task in multi-modal perception that aims to integrate complementary structural and textural cues from different spectral domains. In this paper, we propose FusionNet, a novel end-to-end fusion framework that explicitly models inter-modality interaction and enhances task-critical regions. FusionNet introduces a modality-aware attention mechanism that dynamically adjusts the contribution of infrared and visible features based on their discriminative capacity. To achieve fine-grained, interpretable fusion, we further incorporate a pixel-wise alpha blending module, which learns spatially-varying fusion weights in an adaptive and content-aware manner. Moreover, we formulate a target-aware loss that leverages weak ROI supervision to preserve semantic consistency in regions containing important objects (e.g., pedestrians, vehicles). Experiments on the public M3FD dataset demonstrate that FusionNet generates fused images with enhanced semantic preservation, high perceptual quality, and clear interpretability. Our framework provides a general and extensible solution for semantic-aware multi-modal image fusion, with benefits for downstream tasks such as object detection and scene understanding.