Detection of Underwater Multi-Targets Based on Self-Supervised Learning and Deformable Path Aggregation Feature Pyramid Network

• URL: http://arxiv.org/abs/2505.15518v1
• Date: Wed, 21 May 2025 13:43:26 GMT
• Title: Detection of Underwater Multi-Targets Based on Self-Supervised Learning and Deformable Path Aggregation Feature Pyramid Network
• Authors: Chang Liu,
• Abstract summary: This paper develops a specialized dataset for underwater target detection and proposes an efficient algorithm for underwater multi-target detection.<n>A detection model suitable for underwater target detection is proposed by introducing deformable convolution and dilated convolution.<n>Experiment results show that the accuracy of the underwater target detection has been improved by the proposed detector.
• Score: 3.9561033879611944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To overcome the constraints of the underwater environment and improve the accuracy and robustness of underwater target detection models, this paper develops a specialized dataset for underwater target detection and proposes an efficient algorithm for underwater multi-target detection. A self-supervised learning based on the SimSiam structure is employed for the pre-training of underwater target detection network. To address the problems of low detection accuracy caused by low contrast, mutual occlusion and dense distribution of underwater targets in underwater object detection, a detection model suitable for underwater target detection is proposed by introducing deformable convolution and dilated convolution. The proposed detection model can obtain more effective information by increasing the receptive field. In addition, the regression loss function EIoU is introduced, which improves model performance by separately calculating the width and height losses of the predicted box. Experiment results show that the accuracy of the underwater target detection has been improved by the proposed detector.

Related papers

• Color Information-Based Automated Mask Generation for Detecting Underwater Atypical Glare Areas [0.0]
  This study introduces a breath bubble detection algorithm that utilizes unsupervised K-means clustering.<n>The proposed method fuses color data and relative spatial coordinates from underwater images, employs CLAHE to mitigate noise, and subsequently performs pixel clustering to isolate reflective regions.<n> Experimental results demonstrate that the algorithm can effectively detect regions corresponding to breath bubbles in underwater images, and that the combined use of RGB, LAB, and HSV color spaces significantly enhances detection accuracy.
  arXiv  Detail & Related papers  (2025-02-23T11:17:20Z)
• Nearshore Underwater Target Detection Meets UAV-borne Hyperspectral Remote Sensing: A Novel Hybrid-level Contrastive Learning Framework and Benchmark Dataset [6.832892497880372]
  UAV-borne hyperspectral remote sensing has emerged as a promising approach for underwater target detection (UTD)<n>It is hindered by spectral distortions in nearshore environments, which compromise the accuracy of traditional hyperspectral UTD methods.<n>We propose a novel framework that integrates contrastive learning with a self-paced learning paradigm for robust HUTD in nearshore regions.
  arXiv  Detail & Related papers  (2025-02-20T12:25:30Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Learning Heavily-Degraded Prior for Underwater Object Detection [59.5084433933765]
  This paper seeks transferable prior knowledge from detector-friendly images. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps. Our method with higher speeds and less parameters still performs better than transformer-based detectors.
  arXiv  Detail & Related papers  (2023-08-24T12:32:46Z)
• AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection [40.532331552038485]
  We present a novel Amplitude-Modulated Perturbation and Vortex Convolutional Network, AMSP-UOD. AMSP-UOD addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. Our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity.
  arXiv  Detail & Related papers  (2023-08-23T05:03:45Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Histogram Layer Time Delay Neural Networks for Passive Sonar Classification [58.720142291102135]
  A novel method combines a time delay neural network and histogram layer to incorporate statistical contexts for improved feature learning and underwater acoustic target classification. The proposed method outperforms the baseline model, demonstrating the utility in incorporating statistical contexts for passive sonar target recognition.
  arXiv  Detail & Related papers  (2023-07-25T19:47:26Z)
• Underwater target detection based on improved YOLOv7 [7.264267222876267]
  This study proposes an improved YOLOv7 network (YOLOv7-AC) for underwater target detection. The proposed network utilizes an ACmixBlock module to replace the 3x3 convolution block in the E-ELAN structure. A ResNet-ACmix module is designed to avoid feature information loss and reduce computation.
  arXiv  Detail & Related papers  (2023-02-14T09:50:52Z)
• Watermarking for Out-of-distribution Detection [76.20630986010114]
  Out-of-distribution (OOD) detection aims to identify OOD data based on representations extracted from well-trained deep models. We propose a general methodology named watermarking in this paper. We learn a unified pattern that is superimposed onto features of original data, and the model's detection capability is largely boosted after watermarking.
  arXiv  Detail & Related papers  (2022-10-27T06:12:32Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• A Generative Approach for Detection-driven Underwater Image Enhancement [19.957923413999673]
  We present a model that integrates generative adversarial network (GAN)-based image enhancement with diver detection task. Our proposed approach restructures the GAN objective function to include information from a pre-trained diver detector. We train our network on a large dataset of scuba divers, using a state-of-the-art diver detector, and demonstrate its utility on images collected from oceanic explorations.
  arXiv  Detail & Related papers  (2020-12-10T21:33:12Z)
• Underwater object detection using Invert Multi-Class Adaboost with deep learning [37.14538666012363]
  We propose a novel neural network architecture, namely Sample-WeIghted hyPEr Network (SWIPENet), for small object detection. We show that the proposed SWIPENet+IMA framework achieves better performance in detection accuracy against several state-of-the-art object detection approaches.
  arXiv  Detail & Related papers  (2020-05-23T15:30:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.