MDD-ShipNet: Math-Data Integrated Defogging for Fog-Occlusion Ship Detection
Ning Wang, Yuanyuan Wang, Yuan Feng, Yi Wei
Abstract
For maritime autonomous surface ships, challenges exist in visual detection of ships in sea foggy scenarios, thereby severely degrading visual detection autonomy. In this paper, math-data integrated defogging (MDD) mechanism is created within a ship detection network, termed MDD-ShipNet. Main contributions are as follows: 1) The MDD enhancement module (MDD-EM) is implemented by devising 5 filters, i.e., defog, exposure, tone, contrast and sharpen, as well as a CNN-based parameter learner, such that defogging enhancement can progressively be conducted in a transparent manner; 2) The detector is innovated by employing polarized self-attention (PSA) and weighted bidirectional feature pyramid network (WBiFPN), so as to preserve long-range dependencies and high-resolution channel-spatial features, simultaneously, thereby sufficiently fusing shallow and semantics information associated with contributions to detection; and 3) The entire MDD-ShipNet framework is ultimately established in a weakly supervised manner by integrating MDD-EM and PSA-WBiFPN-based detector, and is fertilized by hybrid dataset that is diversely contributed by real-world and synthesized sea-foggy images. Comprehensive experiments and comparisons eventually validate that the MDD-ShipNet framework outperforms typical approaches deriving from the detection after image enhancement, multi-task learning and domain adaption in terms of [email protected], [email protected]:.95 and FPS.