A method for detecting dead fish on large water surfaces based on improved YOLOv10

• URL: http://arxiv.org/abs/2409.00388v1
• Date: Sat, 31 Aug 2024 08:43:37 GMT
• Title: A method for detecting dead fish on large water surfaces based on improved YOLOv10
• Authors: Qingbin Tian, Yukang Huo, Mingyuan Yao, Haihua Wang,
• Abstract summary: Dead fish can cause significant issues such as water quality deterioration, ecosystem damage, and disease transmission. This paper proposes an end-to-end detection model built upon an enhanced YOLOv10 framework.
• Score: 0.6874745415692134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dead fish frequently appear on the water surface due to various factors. If not promptly detected and removed, these dead fish can cause significant issues such as water quality deterioration, ecosystem damage, and disease transmission. Consequently, it is imperative to develop rapid and effective detection methods to mitigate these challenges. Conventional methods for detecting dead fish are often constrained by manpower and time limitations, struggling to effectively manage the intricacies of aquatic environments. This paper proposes an end-to-end detection model built upon an enhanced YOLOv10 framework, designed specifically to swiftly and precisely detect deceased fish across extensive water surfaces.Key enhancements include: (1) Replacing YOLOv10's backbone network with FasterNet to reduce model complexity while maintaining high detection accuracy; (2) Improving feature fusion in the Neck section through enhanced connectivity methods and replacing the original C2f module with CSPStage modules; (3) Adding a compact target detection head to enhance the detection performance of smaller objects. Experimental results demonstrate significant improvements in P(precision), R(recall), and AP(average precision) compared to the baseline model YOLOv10n. Furthermore, our model outperforms other models in the YOLO series by significantly reducing model size and parameter count, while sustaining high inference speed and achieving optimal AP performance. The model facilitates rapid and accurate detection of dead fish in large-scale aquaculture systems. Finally, through ablation experiments, we systematically analyze and assess the contribution of each model component to the overall system performance.

Related papers

• LAM-YOLO: Drones-based Small Object Detection on Lighting-Occlusion Attention Mechanism YOLO [0.9062164411594178]
  LAM-YOLO is an object detection model specifically designed for drone-based images. We introduce a light-occlusion attention mechanism to enhance the visibility of small targets under different lighting conditions. Second, we utilize an improved SIB-IoU as the regression loss function to accelerate model convergence and enhance localization accuracy.
  arXiv  Detail & Related papers  (2024-11-01T10:00:48Z)
• FMRFT: Fusion Mamba and DETR for Query Time Sequence Intersection Fish Tracking [3.599033310931609]
  This paper establishes a complex multi-scenario sturgeon tracking dataset. It introduces the FMRFT model, a real-time end-to-end fish tracking solution. The model incorporates the low video memory consumption Mamba In Mamba architecture.
  arXiv  Detail & Related papers  (2024-09-02T10:33:45Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection [54.545054873239295]
  Deepfakes have recently raised significant trust issues and security concerns among the public. ViT-based methods take advantage of the expressivity of transformers, achieving superior detection performance. This work introduces Mixture-of-Experts modules for Face Forgery Detection (MoE-FFD), a generalized yet parameter-efficient ViT-based approach.
  arXiv  Detail & Related papers  (2024-04-12T13:02:08Z)
• FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU Matching [11.39414015803651]
  FishMOT is a novel fish tracking approach combining object detection and objectoU matching. The method exhibits excellent robustness and generalizability for varying environments and fish numbers.
  arXiv  Detail & Related papers  (2023-09-06T13:16:41Z)
• 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)
• DeepSeaNet: Improving Underwater Object Detection using EfficientDet [0.0]
  This project involves implementing and evaluating various object detection models on an annotated underwater dataset. The dataset comprises annotated image sequences of fish, crabs, starfish, and other aquatic animals captured in Limfjorden water with limited visibility. I compare the results of YOLOv3 (31.10% mean Average Precision (mAP)), YOLOv4 (83.72% mAP), YOLOv5 (97.6%), YOLOv8 (98.20%), EfficientDet (98.56% mAP) and Detectron2 (95.20% mAP) on the same dataset.
  arXiv  Detail & Related papers  (2023-05-26T13:41:35Z)
• 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)
• StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
  We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception. We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy. Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
  arXiv  Detail & Related papers  (2022-07-21T12:03:02Z)
• Efficient Test-Time Model Adaptation without Forgetting [60.36499845014649]
  Test-time adaptation seeks to tackle potential distribution shifts between training and testing data. We propose an active sample selection criterion to identify reliable and non-redundant samples. We also introduce a Fisher regularizer to constrain important model parameters from drastic changes.
  arXiv  Detail & Related papers  (2022-04-06T06:39:40Z)

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.