Unsupervised adversarial domain adaptation based on interpolation image for fish detection in aquaculture

Zhao, Tengyun; Shen, Zhencai; Zou, Hui; Zhong, Ping; Chen, Yingyi

doi:10.1016/j.compag.2022.107004

Cited by 11 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second stage is represented by models such as R-CNN [13], with high detection accuracy and slow speed. Zhao et al [14] proposed an unsupervised adversarial domain-adaptive fish detection model based on interpolation that combines Faster R-CNN and three adaptive modules to achieve cross-domain detection of fish in different aquaculture environments. Mathur et al [15] proposed a method for fish species classification in underwater images based on migrating ResNet-50 weightoptimized convolutional neural networks.…”

Section: Related Workmentioning

confidence: 99%

Triple Attention Mechanism with YOLOv5s for Fish Detection

Long,

Wang,

et al. 2024

Fishes

View full text Add to dashboard Cite

Traditional fish farming methods suffer from backward production, low efficiency, low yield, and environmental pollution. As a result of thorough research using deep learning technology, the industrial aquaculture model has experienced gradual maturation. A variety of complex factors makes it difficult to extract effective features, which results in less-than-good model performance. This paper proposes a fish detection method that combines a triple attention mechanism with a You Only Look Once (TAM-YOLO)model. In order to enhance the speed of model training, the process of data encapsulation incorporates positive sample matching. An exponential moving average (EMA) is incorporated into the training process to make the model more robust, and coordinate attention (CA) and a convolutional block attention module are integrated into the YOLOv5s backbone to enhance the feature extraction of channels and spatial locations. The extracted feature maps are input to the PANet path aggregation network, and the underlying information is stacked with the feature maps. The method improves the detection accuracy of underwater blurred and distorted fish images. Experimental results show that the proposed TAM-YOLO model outperforms YOLOv3, YOLOv4, YOLOv5s, YOLOv5m, and SSD, with a mAP value of 95.88%, thus providing a new strategy for fish detection.

show abstract

Section: Related Workmentioning

confidence: 99%

Triple Attention Mechanism with YOLOv5s for Fish Detection

Long,

Wang,

et al. 2024

Fishes

View full text Add to dashboard Cite

show abstract

“…These model constructions mainly rely on manual experience to extract features from optical images, leading to the accuracy of subsequent evaluation models being influenced by the adequacy and validity of feature generation. Recently, some studies using deep neural networks for fish detection have been reported [ 25 , 26 ], which automatically extract features through deep learning to maximize the potential features of the data, resulting in significant improvements in detection accuracy and robustness, but these models are complex and usually need to be installed on computational resources with advanced performance.…”

Section: Introductionmentioning

confidence: 99%

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection

Pan,

Huang,

Zhu

et al. 2024

Sensors

View full text Add to dashboard Cite

Rapid detection of fish freshness is of vital importance to ensuring the safety of aquatic product consumption. Currently, the widely used optical detecting methods of fish freshness are faced with multiple challenges, including low detecting efficiency, high cost, large size and low integration of detecting equipment. This research aims to address these issues by developing a low-cost portable fluorescence imaging device for rapid fish freshness detection. The developed device employs ultraviolet-light-emitting diode (UV-LED) lamp beads (365 nm, 10 W) as excitation light sources, and a low-cost field programmable gate array (FPGA) board (model: ZYNQ XC7Z020) as the master control unit. The fluorescence images captured by a complementary metal oxide semiconductor (CMOS) camera are processed by the YOLOv4-Tiny model embedded in FPGA to obtain the ultimate results of fish freshness. The circuit for the YOLOv4-Tiny model is optimized to make full use of FPGA resources and to increase computing efficiency. The performance of the device is evaluated by using grass carp fillets as the research object. The average accuracy of freshness detection reaches up to 97.10%. Moreover, the detection time of below 1 s per sample and the overall power consumption of 47.1 W (including 42.4 W light source power consumption) indicate that the device has good real-time performance and low power consumption. The research provides a potential tool for fish freshness evaluation in a low-cost and rapid manner.

show abstract

“…Zhao et al introduced the domain adaptive object detection into aquaculture field to improve the crossdomain robustness of fish detection. Compared with the original Faster RCNN and domain adaptation model DA-Faster, the proposed method can not only save the cost of manual annotation, but also effectively improve the detection performance of unlabeled target domain [22].…”

Section: Introductionmentioning

confidence: 99%

A cross-domain fruit classification method based on lightweight attention networks and unsupervised domain adaptation

Wang

Zhang

Yan

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

Image-based fruit classification offers many useful applications in industrial production and daily life, such as self-checkout in the supermarket, automatic fruit sorting and dietary guidance. However, fruit classification task will have different data distributions due to different application scenarios. One feasible solution to solve this problem is to use domain adaptation that adapts knowledge from the original training data (source domain) to the new testing data (target domain). In this paper, we propose a novel deep learning-based unsupervised domain adaptation method for cross-domain fruit classification. A hybrid attention module is proposed and added to MobileNet V3 to construct the HAM-MobileNet that can suppress the impact of complex backgrounds and extract more discriminative features. A hybrid loss function combining subdomain alignment and implicit distribution metrics is used to reduce domain discrepancy during model training and improve model classification performance. Two fruit classification datasets covering several domains are established to simulate common industrial and daily life application scenarios. We validate the proposed method on our constructed grape classification dataset and general fruit classification dataset. The experimental results show that the proposed method achieves an average accuracy of 95.0% and 93.2% on the two datasets, respectively. The classification model after domain adaptation can well overcome the domain discrepancy brought by different fruit classification scenarios. Meanwhile, the proposed datasets and method can serve as a benchmark for future cross-domain fruit classification research.

show abstract

Unsupervised adversarial domain adaptation based on interpolation image for fish detection in aquaculture

Cited by 11 publications

References 30 publications

Triple Attention Mechanism with YOLOv5s for Fish Detection

Triple Attention Mechanism with YOLOv5s for Fish Detection

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection

A cross-domain fruit classification method based on lightweight attention networks and unsupervised domain adaptation

Contact Info

Product

Resources

About