Multi-scale fusion and efficient feature extraction for enhanced sonar image object detection

Abstract

Sonar imaging is an underwater detection technology that relies on the transmission and reception of acoustic pulse waves. This technique plays a crucial role in various domains including underwater archaeology, energy exploration, and oceanographic surveying. A primary challenge in sonar imaging is the low signal-to-noise ratio and significant noise interference, issues that are influenced by the constraints of equipment performance and the underwater environment. Traditional object detection techniques have been inadequate in effectively extracting deep features from sonar images possessing targets with complex structures and have also demonstrated shortcomings in the fusion processing of target features at multiple scales, thereby affecting the robustness and accuracy of object detection. To improve the performance of sonar image object detection, we propose an advanced detection framework that integrates efficient feature extraction with multi-scale feature fusion. We employ EfficientNet as the backbone network. EfficientNet exhibits excellent feature extraction capabilities through comprehensive adjustments of depth, width, and resolution. We introduce a dual-channel attention module that blends Squeeze-and-Excitation (SE) and Efficient Channel Attention (ECA) mechanisms to amplify the expression of crucial feature channels and suppress the lesser ones. Additionally, we utilize a modified bidirectional feature pyramid network (BiFPN) to strengthen the integration of features across different layers. Employing these methods, we amalgamate the features into a shared weights classification network and bounding box prediction network for accurate target class discernment and localization. The experimental outcomes provide evidence of the notable superlative nature of the proposed framework in sonar image object detection, effectively ameliorating detection performance amidst noise interference.