Underwater Waste Detection Using Deep Learning A Performance Comparison of YOLOv7 to 10 and Faster RCNN

• URL: http://arxiv.org/abs/2507.18967v1
• Date: Fri, 25 Jul 2025 05:36:37 GMT
• Title: Underwater Waste Detection Using Deep Learning A Performance Comparison of YOLOv7 to 10 and Faster RCNN
• Authors: UMMPK Nawarathne, HMNS Kumari, HMLS Kumari,
• Abstract summary: We investigated the performance of five cutting-edge object recognition algorithms, including YOLOv7, YOLOv8, YOLOv9, and Faster Region-Convolutional Neural Network (R-CNN)<n>YOLOv8 outperformed the others, with a mean Average Precision (mAP) of 80.9%, indicating a significant performance.<n>These findings highlight the YOLOv8 model's potential as an effective tool in the global fight against pollution.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Underwater pollution is one of today's most significant environmental concerns, with vast volumes of garbage found in seas, rivers, and landscapes around the world. Accurate detection of these waste materials is crucial for successful waste management, environmental monitoring, and mitigation strategies. In this study, we investigated the performance of five cutting-edge object recognition algorithms, namely YOLO (You Only Look Once) models, including YOLOv7, YOLOv8, YOLOv9, YOLOv10, and Faster Region-Convolutional Neural Network (R-CNN), to identify which model was most effective at recognizing materials in underwater situations. The models were thoroughly trained and tested on a large dataset containing fifteen different classes under diverse conditions, such as low visibility and variable depths. From the above-mentioned models, YOLOv8 outperformed the others, with a mean Average Precision (mAP) of 80.9%, indicating a significant performance. This increased performance is attributed to YOLOv8's architecture, which incorporates advanced features such as improved anchor-free mechanisms and self-supervised learning, allowing for more precise and efficient recognition of items in a variety of settings. These findings highlight the YOLOv8 model's potential as an effective tool in the global fight against pollution, improving both the detection capabilities and scalability of underwater cleanup operations.

Related papers

• Underlying Semantic Diffusion for Effective and Efficient In-Context Learning [113.4003355229632]
  Underlying Semantic Diffusion (US-Diffusion) is an enhanced diffusion model that boosts underlying semantics learning, computational efficiency, and in-context learning capabilities.<n>We present a Feedback-Aided Learning (FAL) framework, which leverages feedback signals to guide the model in capturing semantic details.<n>We also propose a plug-and-play Efficient Sampling Strategy (ESS) for dense sampling at time steps with high-noise levels.
  arXiv  Detail & Related papers  (2025-03-06T03:06:22Z)
• Plastic Waste Classification Using Deep Learning: Insights from the WaDaBa Dataset [0.0]
  This study focuses on convolutional neural networks (CNNs) and object detection models like YOLO (You Only Look Once)<n>The study shows that YOLO- 11m achieved highest accuracy (98.03%) and mAP50 (0.990), with YOLO-11n performing similarly but highest mAP50(0.992)<n>Lightweight models like YOLO-10n trained faster but with lower accuracy, whereas MobileNet V2 showed impressive performance (97.12% accuracy) but fell short in object detection.
  arXiv  Detail & Related papers  (2024-12-28T18:00:52Z)
• YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision [0.6662800021628277]
  This paper focuses on the evolution of the YOLO (You Only Look Once) object detection algorithm, focusing on YOLOv5, YOLOv8, and YOLOv10. We analyze the architectural advancements, performance improvements, and suitability for edge deployment across these versions.
  arXiv  Detail & Related papers  (2024-07-03T10:40:20Z)
• YOLO9tr: A Lightweight Model for Pavement Damage Detection Utilizing a Generalized Efficient Layer Aggregation Network and Attention Mechanism [0.0]
  This paper proposes YOLO9tr, a novel lightweight object detection model for pavement damage detection. YOLO9tr is based on the YOLOv9 architecture, incorporating a partial attention block that enhances feature extraction and attention mechanisms. The model achieves a high frame rate of up to 136 FPS, making it suitable for real-time applications such as video surveillance and automated inspection systems.
  arXiv  Detail & Related papers  (2024-06-17T06:31:43Z)
• YOLOv10: Real-Time End-to-End Object Detection [68.28699631793967]
  YOLOs have emerged as the predominant paradigm in the field of real-time object detection. The reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs. We introduce the holistic efficiency-accuracy driven model design strategy for YOLOs.
  arXiv  Detail & Related papers  (2024-05-23T11:44:29Z)
• YOLOv5 vs. YOLOv8 in Marine Fisheries: Balancing Class Detection and Instance Count [0.0]
  This paper presents a comparative study of object detection using YOLOv5 and YOLOv8 for three distinct classes: artemia, cyst, and excrement. YOLOv5 often performed better in detecting Artemia and cysts with excellent precision and accuracy. However, when it came to detecting excrement, YOLOv5 faced notable challenges and limitations.
  arXiv  Detail & Related papers  (2024-04-01T20:01:04Z)
• YOLO-World: Real-Time Open-Vocabulary Object Detection [87.08732047660058]
  We introduce YOLO-World, an innovative approach that enhances YOLO with open-vocabulary detection capabilities. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. YOLO-World achieves 35.4 AP with 52.0 FPS on V100, which outperforms many state-of-the-art methods in terms of both accuracy and speed.
  arXiv  Detail & Related papers  (2024-01-30T18:59:38Z)
• YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection [63.36722419180875]
  We provide an efficient and performant object detector, termed YOLO-MS.<n>We train our YOLO-MS on the MS COCO dataset from scratch without relying on any other large-scale datasets.<n>Our work can also serve as a plug-and-play module for other YOLO models.
  arXiv  Detail & Related papers  (2023-08-10T10:12:27Z)
• 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)
• A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery [94.78943497436492]
  We present YOLO-S, a simple, fast and efficient network for small target detection. YOLO-S exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation. YOLO-S has an 87% decrease of parameter size and almost one half FLOPs of YOLOv3, making practical the deployment for low-power industrial applications.
  arXiv  Detail & Related papers  (2022-04-05T16:29:49Z)
• To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection [95.21700830273221]
  Weakly-supervised salient object detection (WSOD) aims to develop saliency models using image-level annotations. We propose a self-calibrated training strategy by explicitly establishing a mutual calibration loop between pseudo labels and network predictions. We prove that even a much smaller dataset with well-matched annotations can facilitate models to achieve better performance as well as generalizability.
  arXiv  Detail & Related papers  (2021-09-04T02:45:22Z)

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.