<p>Maritime visual object detection based on Unmanned Surface Vehicles (USVs) is a pivotal technology for intelligent maritime surveillance. However, images acquired from the sea surface frequently encounter challenges such as object loss due to low visibility and uncertainty in object scales, which severely degrade detection performance. To address these challenges, this paper proposes the MaritimeV8 object detection framework for USV-based maritime monitoring. Specifically, a lightweight multi-scale receptive field modeling dual-domain hybrid backbone network, MHDENet, is designed to improve degraded maritime image quality through dynamic frequency-domain recalibration and enable efficient feature extraction. Subsequently, a novel lightweight Bidirectional Pyramid Aggregation Network (Light-BiPANet) is constructed to effectively fuse multi-scale object features. Additionally, a Coordinate Attention (CA) module is introduced to enhance attention on object detection regions. Finally, MaritimeV8 is evaluated and compared with other mainstream detectors on the SeaShips and Singapore Maritime Dataset (SMD). The results indicate that MaritimeV8 significantly enhances multi-scale object detection in low-visibility maritime scenarios. It achieves a 2.1% mAP improvement on SMD compared to the baseline YOLOV8n network, with only a marginal increase of 3.7M parameters, while maintaining a real-time performance of 24.7 FPS. This realizes a superior synergistic balance among accuracy, efficiency, and lightweightness, thereby demonstrating pronounced advantages for maritime monitoring under challenging conditions of low visibility and uncertain object scales.</p>