DMAM: Dynamic Multiscale Adaptive Mechanism-Driven Remote-Sensing Target Detection Network

Remote-sensing small-object detection offers significant advantages and holds great importance in monitoring and measurement fields such as scene perception and environmental monitoring. Although deep neural networks have advanced the development of remote-sensing object detection, challenges remain, including large-scale variations and the difficulty of detecting dense, minute objects. To address these challenges, we propose a dynamic multiscale adaptive mechanism-driven remote-sensing object detection network (DMAM). First, to overcome the inherent limitations of traditional convolutional fixed sampling positions and uniform parameter distributions, we introduce adaptive kernel convolution (AKConv). By dynamically adjusting sampling positions and optimizing parameter distributions, AKConv enables more flexible and efficient feature extraction. Second, to effectively leverage prior spatial knowledge for expanding the receptive field, we propose a dynamic multiscale context adaptation (DMCA) module. This module implements a content-aware dynamic gating mechanism, which adaptively allocates weights between local details and global context by analyzing image content at each spatial location. By integrating local features with global information, it enhances scene comprehension, thereby improving detection accuracy and robustness. Finally, a shape-aware metric function (Shape-IoU) is introduced, which incorporates a shape-adaptive weighting mechanism to dynamically adjust the penalty weights for different geometric factors based on the target’s own shape characteristics, thereby achieving more precise bounding box regression. Results on three public datasets show that the proposed method demonstrates robust performance compared to state-of-the-art detection networks. Specifically, DMAM achieves an average accuracy of 97.7% on the RSOD dataset, 92.5% on the NWPU VHR-10 dataset, and 88.0% on the DIOR dataset.