LSFDNet: A Single-Stage Fusion and Detection Network for Ships Using SWIR and LWIR

TL;DR

LSFDNet is a novel single-stage fusion and detection network that combines SWIR and LWIR images for improved ship detection in complex scenarios, utilizing advanced feature fusion modules and a new dataset.

Contribution

The paper introduces LSFDNet, a new single-stage fusion detection network that effectively integrates SWIR and LWIR modalities with novel modules and a new dataset for ship detection.

Findings

Superior detection performance on two datasets.

Effective fusion of SWIR and LWIR images.

Enhanced object saliency and semantic retention.

Abstract

Traditional ship detection methods primarily rely on single-modal approaches, such as visible or infrared images, which limit their application in complex scenarios involving varying lighting conditions and heavy fog. To address this issue, we explore the advantages of short-wave infrared (SWIR) and long-wave infrared (LWIR) in ship detection and propose a novel single-stage image fusion detection algorithm called LSFDNet. This algorithm leverages feature interaction between the image fusion and object detection subtask networks, achieving remarkable detection performance and generating visually impressive fused images. To further improve the saliency of objects in the fused images and improve the performance of the downstream detection task, we introduce the Multi-Level Cross-Fusion (MLCF) module. This module combines object-sensitive fused features from the detection task and aggregates features across multiple modalities, scales, and tasks to obtain more semantically rich fused features. Moreover, we utilize the position prior from the detection task in the Object Enhancement (OE) loss function, further increasing the retention of object semantics in the fused images. The detection task also utilizes preliminary fused features from the fusion task to complement SWIR and LWIR features, thereby enhancing detection performance. Additionally, we have established a Nearshore Ship Long-Short Wave Registration (NSLSR) dataset to train effective SWIR and LWIR image fusion and detection networks, bridging a gap in this field. We validated the superiority of our proposed single-stage fusion detection algorithm on two datasets. The source code and dataset are available at https://github.com/Yanyin-Guo/LSFDNet