Graph Convolutional Network Aggregation For Broad-Spectral Object Detection

Object detection is a critical component in various applications, yet conventional detection architectures are typically developed on RGB or grayscale databases, limiting their effectiveness in challenging real-world scenarios. To address this, this work utilizes broad-spectral databases to enhance detection in low-light and complex environments. We introduce a graph-based representation that captures both in-channel and cross-channel features in broad-spectral images, enabling a more comprehensive scene understanding. A Graph Convolutional Network (GCN)-based aggregation strategy is proposed to integrate information from multiple channels effectively. A data enhancement method is also proposed based on the imbalanced characteristics in the foreground and background regions. Experimental results show that our model achieves superior mean Average Precision (mAP@50) across multiple datasets.