Embedded Online Fish Detection and Tracking System via YOLOv3 and Parallel Correlation Filter

Liu, Shasha; Li, Xiaoyü; Gao, Mingshan; Cai, Yu; Nian, Rui; Li, Peiliang; Yan, Tianhong; Lendasse, Amaury

doi:10.1109/oceans.2018.8604658

Cited by 33 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Then, there comes one-stage detection algorithms with high detection speed. R-CNN and YOLO are currently the most widely utilised one-stage object detection methods (You Only Look Once) [7] [8][9] [10].…”

Section: Object Detectionmentioning

confidence: 99%

An Analysis of Defect Detection on Steel Strip Surface Using Modified Yolo V5

Prabadevi

2022

Preprint

View full text Add to dashboard Cite

Defects in any type of material do lessen the strength and will diminish the service life which in-turn affects the quality. Steel surface images with broad scale variance and complicated background lessens the high quality and productiveness. In steel surface images detection, there are hitches of small target sizes and imprecise features that causes incorrect and ignored detection. Nevertheless, these problems can be avoided with the aid of inspecting the defects earlier than processing with accurate and automated object detection algorithms. YOLOv5, a fine tuned learning model is evaluated on available steel strip datasets, which solves identification and detection problems in industrial scenario. The current YOLOv5 research focus on improving accuracy on defect classification and detection speed however there’s no distinct records on algorithm execution time and size. This study focus on YOLOv5 model with induced GHOST and TRANSFORMER modules. Through the new modules, we are looking to reduce the size of the algorithm, number of parameters and number of GFLOPS. By these changes this model may be deployed easily on mobile, embedded devices for detection of small, easily confused defects.

show abstract

Section: Object Detectionmentioning

confidence: 99%

An Analysis of Defect Detection on Steel Strip Surface Using Modified Yolo V5

Prabadevi

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Sung et al [45] used YOLO for fish detection and achieved 16.7 fps object detection speed in GPU. In work [46], YOLOv3 was implemented on NVIDIA Jetson TX2 and the experimental results showed that it can achieve real-time object detection. However, the above research does not take any effective approaches to enhance the quality of underwater images before performing object detection.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time FPGA Accelerator Based on Winograd Algorithm for Underwater Object Detection

Cai

Wang

2021

Electronics

View full text Add to dashboard Cite

Real-time object detection is a challenging but crucial task for autonomous underwater vehicles because of the complex underwater imaging environment. Resulted by suspended particles scattering and wavelength-dependent light attenuation, underwater images are always hazy and color-distorted. To overcome the difficulties caused by these problems to underwater object detection, an end-to-end CNN network combined U-Net and MobileNetV3-SSDLite is proposed. Furthermore, the FPGA implementation of various convolution in the proposed network is optimized based on the Winograd algorithm. An efficient upsampling engine is presented, and the FPGA implementation of squeeze-and-excitation module in MobileNetV3 is optimized. The accelerator is implemented on a Zynq XC7Z045 device running at 150 MHz and achieves 23.68 frames per second (fps) and 33.14 fps when using MobileNetV3-Large and MobileNetV3-Small as the feature extractor. Compared to CPU, our accelerator achieves 7.5×–8.7× speedup and 52×–60× energy efficiency.

show abstract

“…In previous work on fish detection, Liu et al [18] have presented an online fish tracking system using YOLO and parallel correlation filters, and included detection and categorization in an end-to-end approach. Similar work is carried out by Xu et al [19] who trained a YOLO architecture aimed at detecting a variety of fish species with three very different datasets, obtaining a mean average precision score of 0.5392.…”

Section: Introductionmentioning

confidence: 99%

Temperate fish detection and classification: a deep learning based approach

Wiklund²,

et al. 2021

View full text Add to dashboard Cite

A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) object detection technique. In the second step, we adopt a Convolutional Neural Network (CNN) with the Squeeze-and-Excitation (SE) architecture for classifying each fish in the image without pre-filtering. We apply transfer learning to overcome the limited training samples of temperate fishes and to improve the accuracy of the classification. This is done by training the object detection model with ImageNet and the fish classifier via a public dataset (Fish4Knowledge), whereupon both the object detection and classifier are updated with temperate fishes of interest. The weights obtained from pre-training are applied to post-training as a priori. Our solution achieves the state-of-the-art accuracy of 99.27% using the pre-training model. The accuracies using the post-training model are also high; 83.68% and 87.74% with and without image augmentation, respectively. This strongly indicates that the solution is viable with a more extensive dataset.

show abstract

Embedded Online Fish Detection and Tracking System via YOLOv3 and Parallel Correlation Filter

Cited by 33 publications

References 8 publications

An Analysis of Defect Detection on Steel Strip Surface Using Modified Yolo V5

An Analysis of Defect Detection on Steel Strip Surface Using Modified Yolo V5

A Real-Time FPGA Accelerator Based on Winograd Algorithm for Underwater Object Detection

Temperate fish detection and classification: a deep learning based approach

Contact Info

Product

Resources

About