Design and analysis of a novel sonar-based obstacle-avoidance system for the visually impaired and unmanned systems

Raghuvanshi, Digvija S.; Dutta, Isha; Vaidya, Ravindra

doi:10.1109/embeddedsys.2014.6953165

Cited by 7 publications

(3 citation statements)

References 8 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sonar target detection methods can be categorized into traditional techniques and Convolutional Neural Network (CNN)-based approaches. Conventional sonar image detection methods predominantly employ pixel-based (Chen et al, 2014), feature-based (Mukherjee et al, 2011), and echo-based (Raghuvanshi et al, 2014) strategies. These methods utilize manually crafted filters founded on pixel value characteristics, grayscale thresholds, or a priori information about the targets for detection.…”

Section: Introductionmentioning

confidence: 99%

Underwater small target detection based on dynamic convolution and attention mechanism

Cheng,

Wang,

Yang

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

In ocean observation missions, unmanned autonomous ocean observation platforms play a crucial role, with precise target detection technology serving as a key support for the autonomous operation of unmanned platforms. Among various underwater sensing devices, side-scan sonar (SSS) has become a primary tool for wide-area underwater detection due to its extensive detection range. However, current research on target detection with SSS primarily focuses on large targets such as sunken ships and aircraft, lacking investigations into small targets. In this study, we collected data on underwater small targets using an unmanned boat equipped with SSS and proposed an enhancement method based on the YOLOv7 model for detecting small targets in SSS images. First, to obtain more accurate initial anchor boxes, we replaced the original k-means algorithm with the k-means++ algorithm. Next, we replaced ordinary convolution blocks in the backbone network with Omni-dimensional Dynamic Convolution (ODConv) to enhance the feature extraction capability for small targets. Subsequently, we inserted a Global Attention Mechanism (GAM) into the neck network to focus on global information and extract target features, effectively addressing the issue of sparse target features in SSS images. Finally, we mitigated the harmful gradients produced by low-quality annotated data by adopting Wise-IoU (WIoU) to improve the detection accuracy of small targets in SSS images. Through validation on the test set, the proposed method showed a significant improvement compared to the original YOLOv7, with increases of 5.05% and 2.51% in mAP@0.5 and mAP@0.5: 0.95 indicators, respectively. The proposed method demonstrated excellent performance in detecting small targets in SSS images and can be applied to the detection of underwater mines and small equipment, providing effective support for underwater small target detection tasks.

show abstract

Section: Introductionmentioning

confidence: 99%

Underwater small target detection based on dynamic convolution and attention mechanism

Cheng,

Wang,

Yang

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

“…In the past decades, the traditional underwater target detection methods are mainly used for sonar image analysis. These methods include detection based on pixel characteristics, gray value or the prior knowledge of the target [1][2][3][4][5][6]. Due to the complexity of underwater environment, the quality of sonar image is seriously affected by its own noise, reverberation and environmental noise, resulting in low image resolution, unclear edge details, and significant speckle noise.…”

Section: Introductionmentioning

confidence: 99%

CCW-YOLOv5: A forward-looking sonar target method based on coordinate convolution and modified boundary frame loss

Sun,

Yin

2024

PLoS ONE

View full text Add to dashboard Cite

Multi beam forward looking sonar plays an important role in underwater detection. However, due to the complex underwater environment, unclear features, and susceptibility to noise interference, most forward looking sonar systems have poor recognition performance. The research on MFLS for underwater target detection faces some challenges. Therefore, this study proposes innovative improvements to the YOLOv5 algorithm to address the above issues. On the basis of maintaining the original YOLOv5 architecture, this improved model introduces transfer learning technology to overcome the limitation of scarce sonar image data. At the same time, by incorporating the concept of coordinate convolution, the improved model can extract features with rich positional information, significantly enhancing the model’s detection ability for small underwater targets. Furthermore, in order to solve the problem of feature extraction in forward looking sonar images, this study integrates attention mechanisms. This mechanism expands the receptive field of the model and optimizes the feature learning process by highlighting key details while suppressing irrelevant information. These improvements not only enhance the recognition accuracy of the model for sonar images, but also enhance its applicability and generalization performance in different underwater environments. In response to the common problem of uneven training sample quality in forward looking sonar imaging technology, this study made a key improvement to the classic YOLOv5 algorithm. By adjusting the bounding box loss function of YOLOv5, the model’s over sensitivity to low-quality samples was reduced, thereby reducing the punishment on these samples. After a series of comparative experiments, the newly proposed CCW-YOLOv5 algorithm has achieved detection accuracy in object detection mAP@0.5 Reached 85.3%, and the fastest inference speed tested on the local machine was 54 FPS, showing significant improvement and performance improvement compared to existing advanced algorithms.

show abstract

“…In the past decades, sonar images have been detected by artificial feature extraction, mainly based on pixel [1], [2], feature [3], [4] and echo [5], [6]. Most of this traditional method of underwater target detection based on pixel values features, gray threshold, or prior information of corresponding targets.…”

Section: Introductionmentioning

confidence: 99%

Target Detection of Forward-Looking Sonar Image Based on Improved YOLOv5

et al. 2022

View full text Add to dashboard Cite

Forward-looking sonar is a commonly used underwater detection device at present, but the detection accuracy is not high due to the complex underwater environment, small target highlight area and fuzzy feature details. Therefore, this paper proposes a forward sonar image target detection model based on YOLOv5 network using transfer learning method. Firstly, the YOLOv5 network is pretrained with coco data set. Then the pre-training model is fine-tuned according to the training set of forward-looking sonar images. Before fine-tuning, the traditional k-means clustering is improved. The IoU value is used as the distance function to cluster the labeling information of the training set of the forward-looking sonar image. The results of clustering serve as the initial anchor frame of the training network. This operation greatly improves the detection speed. Secondly, due to the characteristics of weak echo intensity and small target area of forward-looking sonar image, an improved feature extraction method of CoordConv was proposed to give corresponding coordinate information to high-level features which improves the accuracy of network detection regression. Finally, the fine-tuned network is used to detect the target in the forward-looking sonar image. The experimental results show that the improved model based on YOLOv5 network is superior to the original YOLOV5 network and other popular deep neural networks for target detection in the forward-looking sonar image, which has a reference significance for underwater target detection.

show abstract

Design and analysis of a novel sonar-based obstacle-avoidance system for the visually impaired and unmanned systems

Cited by 7 publications

References 8 publications

Underwater small target detection based on dynamic convolution and attention mechanism

Underwater small target detection based on dynamic convolution and attention mechanism

CCW-YOLOv5: A forward-looking sonar target method based on coordinate convolution and modified boundary frame loss

Target Detection of Forward-Looking Sonar Image Based on Improved YOLOv5

Contact Info

Product

Resources

About