An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces.It has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of m...An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces.It has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine life.Underwater Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions.In this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies.Several models have been developed earlier for fish detection.However,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different genus.With this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN model.The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded environments.IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish detection.Besides,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is applied.This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions.Finally,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier model.The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under different aspects with the maximum accuracy of 98%and 97%on the applied blurred and crowded datasets respectively.展开更多
Fish finders have already been widely available in the fishing market for a number of years.However,the sizes of these fish finders are too big and their prices are expensive to suit for the research of robotic fish o...Fish finders have already been widely available in the fishing market for a number of years.However,the sizes of these fish finders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine.The goal of this research is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor. With some pre-condition in hardware and algorithms,the experimental results show that the proposed design has good per- formance,with a detection rate of 100 % and a classification rate of 94 %.Both the existing type of fish and the group behavior can be revealed by statistical interpretations such as hovering passion and sparse swimming mode.展开更多
Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for...Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses.To address this issue,an improved algorithm based on the You Only Look Once v5s(YOLOv5s)lightweight model has been proposed.This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module(CBAM)to achieve high recognition accuracy.Furthermore,the model introduces theα-SIoU loss function,which combines theα-Intersection over Union(α-IoU)and Shape Intersection over Union(SIoU)loss functions,thereby improving the accuracy of bounding box regression and object recognition.The average precision of the improved model reaches 94.2%for detecting unhealthy fish,representing increases of 11.3%,9.9%,9.7%,2.5%,and 2.1%compared to YOLOv3-tiny,YOLOv4,YOLOv5s,GhostNet-YOLOv5,and YOLOv7,respectively.Additionally,the improved model positively impacts hardware efficiency,reducing requirements for memory size by 59.0%,67.0%,63.0%,44.7%,and 55.6%in comparison to the five models mentioned above.The experimental results underscore the effectiveness of these approaches in addressing the challenges associated with fish health detection,and highlighting their significant practical implications and broad application prospects.展开更多
In order to establish a new method for the determination of enrofloxacin, on the basis of the fluorescence characteristic of terbium-enrofloxacin complex, it was found that in a HAc-NaAc buffer solution with a pH valu...In order to establish a new method for the determination of enrofloxacin, on the basis of the fluorescence characteristic of terbium-enrofloxacin complex, it was found that in a HAc-NaAc buffer solution with a pH value at 6.0, the terbiumenrofloxacin complex had a characteristic fluorescence peak of Tb3＋ at 545 nm （λex= 328 nm）, which could be used for the determination of enrofloxacin. A simple, rapid and sensitive fluorescence method for the determination of enrofloxacin was thus established, and an optimal reaction condition was selected. Under the optimal reaction condition, there was a good linear relation （r2=0.992 3） between concentration of enrofloxacin in the range of 1.0×10^-1.0×10^-6 g/ml and its fluorescence intensity at 545 nm, with a detection limit of 1.3×10^-9 g/ml; the recovery for enrofloxacin in tablets was 97.7% with a variation coefficient of 1.4%; and for enrofloxacin in fish tissues, the recovery was 79.0%-94.5% with a variation coefficient of 2.0%-7.8%.展开更多
Tiger puffer is a commercially important fish cultured in high-density environments,and its accurate detection is indispensable for determining growth conditions and realizing accurate feeding.However,the detection pr...Tiger puffer is a commercially important fish cultured in high-density environments,and its accurate detection is indispensable for determining growth conditions and realizing accurate feeding.However,the detection precision and recall of farmed tiger puffer are low due to target blurring and occlusion in real farming environments.The farmed tiger puffer detection model,called knowledge aggregation YOLO(KAYOLO),fuses prior knowledge with improved YOLOv5 and was proposed to solve this problem.To alleviate feature loss caused by target blurring,we drew on the human practice of using prior knowledge for reasoning when recognizing blurred targets and used prior knowledge to strengthen the tiger puffer’s features and improve detection precision.To address missed detection caused by mutual occlusion in high-density farming environments,a prediction box aggregation method,aggregating prediction boxes of the same object,was proposed to reduce the influence among different objects to improve detection recall.To validate the effectiveness of the proposed methods,ablation experiments,model performance experiments,and model robustness experiments were designed.The experimental results showed that KAYOLO’s detection precision and recall results reached 94.92% and 92.21%,respectively.The two indices were improved by 1.29% and 1.35%,respectively,compared to those of YOLOv5.Compared with the recent state-of-the-art underwater object detection models,such as SWIPENet,RoIMix,FERNet,and SK-YOLOv5,KAYOLO achieved 2.09%,1.63%,1.13% and 0.85% higher precision and 1.2%,0.18%,1.74% and 0.39% higher recall,respectively.Experiments were conducted on different datasets to verify the model’s robustness,and the precision and recall of KAYOLO were improved by approximately 1.3% compared to those of YOLOv5.The study showed that KAYOLO effectively enhanced farmed tiger puffer detection by reducing blurring and occlusion effects.Additionally,the model had a strong generalization ability on different datasets,indicating that the model can be adapted to different environments,and it has strong robustness.展开更多
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 1/53/42),www.kku.edu.sa.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.
文摘An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces.It has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine life.Underwater Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions.In this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies.Several models have been developed earlier for fish detection.However,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different genus.With this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN model.The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded environments.IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish detection.Besides,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is applied.This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions.Finally,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier model.The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under different aspects with the maximum accuracy of 98%and 97%on the applied blurred and crowded datasets respectively.
基金This research was fund by Fundamental Research Fund Program from Indonesia Ministry of Research and Technology in years 2007. ID Number: RD-2009-2550.
文摘Fish finders have already been widely available in the fishing market for a number of years.However,the sizes of these fish finders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine.The goal of this research is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor. With some pre-condition in hardware and algorithms,the experimental results show that the proposed design has good per- formance,with a detection rate of 100 % and a classification rate of 94 %.Both the existing type of fish and the group behavior can be revealed by statistical interpretations such as hovering passion and sparse swimming mode.
基金supported by The Agricultural Science and Technology Independent Innovation Fund Project of Jiangsu Province(CX(22)3111)the National Natural Science Foundation of China Project(62173162)partly by the Changzhou Science and Technology Support Project(CE20225016).
文摘Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses.To address this issue,an improved algorithm based on the You Only Look Once v5s(YOLOv5s)lightweight model has been proposed.This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module(CBAM)to achieve high recognition accuracy.Furthermore,the model introduces theα-SIoU loss function,which combines theα-Intersection over Union(α-IoU)and Shape Intersection over Union(SIoU)loss functions,thereby improving the accuracy of bounding box regression and object recognition.The average precision of the improved model reaches 94.2%for detecting unhealthy fish,representing increases of 11.3%,9.9%,9.7%,2.5%,and 2.1%compared to YOLOv3-tiny,YOLOv4,YOLOv5s,GhostNet-YOLOv5,and YOLOv7,respectively.Additionally,the improved model positively impacts hardware efficiency,reducing requirements for memory size by 59.0%,67.0%,63.0%,44.7%,and 55.6%in comparison to the five models mentioned above.The experimental results underscore the effectiveness of these approaches in addressing the challenges associated with fish health detection,and highlighting their significant practical implications and broad application prospects.
基金Supported by Scientific Research Fund of Hanshan Normal University(511043)~~
文摘In order to establish a new method for the determination of enrofloxacin, on the basis of the fluorescence characteristic of terbium-enrofloxacin complex, it was found that in a HAc-NaAc buffer solution with a pH value at 6.0, the terbiumenrofloxacin complex had a characteristic fluorescence peak of Tb3＋ at 545 nm （λex= 328 nm）, which could be used for the determination of enrofloxacin. A simple, rapid and sensitive fluorescence method for the determination of enrofloxacin was thus established, and an optimal reaction condition was selected. Under the optimal reaction condition, there was a good linear relation （r2=0.992 3） between concentration of enrofloxacin in the range of 1.0×10^-1.0×10^-6 g/ml and its fluorescence intensity at 545 nm, with a detection limit of 1.3×10^-9 g/ml; the recovery for enrofloxacin in tablets was 97.7% with a variation coefficient of 1.4%; and for enrofloxacin in fish tissues, the recovery was 79.0%-94.5% with a variation coefficient of 2.0%-7.8%.
基金supported by the Key Projects of Educational Department of Liaoning Province(LJKZ0729)National Natural Science Foundation of China(31972846)+1 种基金Liaoning Province Natural Science Foundation(2020-KF-12-09)Foundation of Educational Department of Liaoning Province(LJKZ0730).
文摘Tiger puffer is a commercially important fish cultured in high-density environments,and its accurate detection is indispensable for determining growth conditions and realizing accurate feeding.However,the detection precision and recall of farmed tiger puffer are low due to target blurring and occlusion in real farming environments.The farmed tiger puffer detection model,called knowledge aggregation YOLO(KAYOLO),fuses prior knowledge with improved YOLOv5 and was proposed to solve this problem.To alleviate feature loss caused by target blurring,we drew on the human practice of using prior knowledge for reasoning when recognizing blurred targets and used prior knowledge to strengthen the tiger puffer’s features and improve detection precision.To address missed detection caused by mutual occlusion in high-density farming environments,a prediction box aggregation method,aggregating prediction boxes of the same object,was proposed to reduce the influence among different objects to improve detection recall.To validate the effectiveness of the proposed methods,ablation experiments,model performance experiments,and model robustness experiments were designed.The experimental results showed that KAYOLO’s detection precision and recall results reached 94.92% and 92.21%,respectively.The two indices were improved by 1.29% and 1.35%,respectively,compared to those of YOLOv5.Compared with the recent state-of-the-art underwater object detection models,such as SWIPENet,RoIMix,FERNet,and SK-YOLOv5,KAYOLO achieved 2.09%,1.63%,1.13% and 0.85% higher precision and 1.2%,0.18%,1.74% and 0.39% higher recall,respectively.Experiments were conducted on different datasets to verify the model’s robustness,and the precision and recall of KAYOLO were improved by approximately 1.3% compared to those of YOLOv5.The study showed that KAYOLO effectively enhanced farmed tiger puffer detection by reducing blurring and occlusion effects.Additionally,the model had a strong generalization ability on different datasets,indicating that the model can be adapted to different environments,and it has strong robustness.
