Few-Shot Multi-Class Ship Detection in Remote Sensing Images Using Attention Feature Map and Multi-Relation Detector

Abstract

Monitoring and identification of ships in remote sensing images is of great significance for port management, marine traffic, marine security, etc. However, due to small size and complex background, ship detection in remote sensing images is still a challenging task. Currently, deep-learning-based detection models need a lot of data and manual annotation, while training data containing ships in remote sensing images may be in limited quantities. To solve this problem, in this paper, we propose a few-shot multi-class ship detection algorithm with attention feature map and multi-relation detector (AFMR) for remote sensing images. We use the basic framework of You Only Look Once (YOLO), and use the attention feature map module to enhance the features of the target. In addition, the multi-relation head module is also used to optimize the detection head of YOLO. Extensive experiments on publicly available HRSC2016 dataset and self-constructed REMEX-FSSD dataset validate that our method achieves a good detection performance.

Lightweight ship detection method based on Swin-YOLOFormer

“…proposed a probabilistic ship detection method based on deep learning, introducing Faster R-CNN for feature extraction of ships and determining ship categories through Bayesian fusion. Zhang et al 20 . proposed a ship detection algorithm based on attention feature maps and multiple relationship detectors to address the problem of small object volume and complex background in datasets.…”

“…The improved method effectively reduces the parameters. Zhang et al 20 proposed a ship detection algorithm based on attention feature maps and multiple relationship detectors to address the problem of small object volume and complex background in datasets. The YOLO model detection head was optimized through multiple relationship head modules.…”

Automated VIIRS Boat Detection Based on Machine Learning and Its Application to Monitoring Fisheries in the East China Sea

“…In this study, a two-step machine learning based on existing VBD outputs visually inspected by humans and artificial features (i.e., SMI) was used to tackle these issues. In other major satellite data, such as SAR, approaches using transition learning of deep learning models have been attempted for similar issues [42][43][44]. It may also be worthwhile to try approaches that use deep learning with limited true vessel presence data and no artificial features for VIIRS.…”

Multi-Oriented Enhancement Branch and Context-Aware Module for Few-Shot Oriented Object Detection in Remote Sensing Images

“…Ai et al [37] proposed a SAR image target detection method based on a multi-level depth learning network that fuses the high-level target depth feature. Zhang et al [38] proposed a few-shot ship detection algorithm based on the YOLO algorithm, which achieved a better result. Zhang et al [39] introduced a remote sensing few-shot object detection method based on text semantic fusion relation graph reasoning (TSF-RGR), which learns various types of relationships from common sense knowledge.…”