Comparison of Deep Learning and Image Processing for Tracking the Cognitive Motion of a Laboratory Mouse

Lee, Seohyun; Hayakawa, Tetsuo; Nishimura, Chika; Yawata, Satoshi; Yagi, Hiroki; Watanabe, Dai; Ishikawa, Masatoshi

doi:10.1109/biocas.2019.8919239

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Image processing techniques use segmentation and extract most objects' feature and accurately detect object with less computational resources [33]. Although deep learning has been the state-of-the-art method to detect objects in an image or video due to better accuracy and probability score result [34], it can only be useful with a sufcient training dataset refecting large diversity of target conditions, since decreasing the number of data used will increase its detection error [35]. On-device AI tasks using machine learning also show prominent energy drain for drones and mobile systems in general [36].…”

Section: Image Processing Methods To Detect Defective Pv Modulementioning

confidence: 99%

Fast Partial Shading Detection on PV Modules for Precise Power Loss Ratio Estimation Using Digital Image Processing

Setiawan,

Fathurrahman,

Pamungkas

et al. 2024

Journal of Electrical and Computer Engineering

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Maintaining the maximum performance of solar panels poses the foremost challenge for solar photovoltaic power plants in this era. One of the common PV faults which decreases PV power output is a hot spot which is caused by a prolonged local partial shading from objects, such as dust piles or animal waste. To prevent it, an enormous effort for PV inspection is needed especially for large solar power plants. Hence, automatic partial shading detection is critical in preventing PV hot spots to assist maintenance activities which are associated with a drop in energy output. This research developed fast partial shading detection application on PV modules using digital image processing to detect the hot spot and PV modules areas and afterwards calculate the PV systems power loss ratio. The proposed method demonstrated a hot spot detection rate of 94.74% and a module detection rate of 100%. The power loss ratio calculation is compared and validated using IV curve measurement and has 91.26% similarity value which is a feasible application for the real-world system.

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Section: Image Processing Methods To Detect Defective Pv Modulementioning

confidence: 99%

Fast Partial Shading Detection on PV Modules for Precise Power Loss Ratio Estimation Using Digital Image Processing

Setiawan,

Fathurrahman,

Pamungkas

et al. 2024

Journal of Electrical and Computer Engineering

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“…Recent developments of machine learning and computer vision algorithms have propelled an advancement of the tools to analyze mouse behavior. One of the most important supervised approaches that have been applied to the automated analysis of mouse behavior is deep learning (Graving et al, 2019;Lee et al, 2019;Pereira et al, 2020). This is an area of machine learning that employs artificial neural networks (ANNs), a computational formalism originally inspired in biological brains.…”

Section: Deep Learning Applied To Mouse Behavior Classification In the Homecagementioning

confidence: 99%

Challenges of a small world analysis for the continuous monitoring of behavior in mice

Bermudez-Contreras¹,

Whishaw²,

sutherland³

et al. 2021

Preprint

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The automation of monitoring and analysis of mouse behaviour in a homecage can be obtained from continuous video records with machine learning and computer vision. The approach of recreating a mouse’s “real world” behavior and laboratory test behavior in the “small world” of a laboratory cage can provide insights into phenotypical expression of mouse genotypes, development and aging, and neurological disease. Algorithms identify behavioral acts (walk, rear), actions (sleep duration, distance travelled), organized patterns of movement (home base activity and excursions) over extended periods of time. In addition, performance on specific tests can be incorporated within a mouse’s living arrangement. Here we review approaches to engineering a small world and state of the art machine learning analyses for automated study of mouse homecage behavior. We highlight advantages and limitations of these approaches as a supplement to acute behavioral testing methodology.

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“…Recently, there is a great interest in using machine learning and neural networks to understand social behavior [9][10][11]. Lorbach in [12] presents a rat social interaction dataset with results of using it as a training set for a method of recognizing interactions with rats.…”

Section: Introductionmentioning

confidence: 99%

Detection and Model of Thermal Traces Left after Aggressive Behavior of Laboratory Rodents

et al. 2021

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Automation of complex social behavior analysis of experimental animals would allow for faster, more accurate and reliable research results in many biological, pharmacological, and medical fields. However, there are behaviors that are not only difficult to detect for the computer, but also for the human observer. Here, we present an analysis of the method for identifying aggressive behavior in thermal images by detecting traces of saliva left on the animals’ fur after a bite, nape attack, or grooming. We have checked the detection capabilities using simulations of social test conditions inspired by real observations and measurements. Detection of simulated traces different in size and temperature on single original frame revealed the dependence of the parameters of commonly used corner detectors (R score, ranking) on the parameters of the traces. We have also simulated temperature of saliva changes in time and proved that the detection time does not affect the correctness of the approximation of the observed process. Furthermore, tracking the dynamics of temperature changes of these traces allows to conclude about the exact moment of the aggressive action. In conclusion, the proposed algorithm together with thermal imaging provides additional data necessary to automate the analysis of social behavior in rodents.

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Comparison of Deep Learning and Image Processing for Tracking the Cognitive Motion of a Laboratory Mouse

Cited by 6 publications

References 18 publications

Fast Partial Shading Detection on PV Modules for Precise Power Loss Ratio Estimation Using Digital Image Processing

Fast Partial Shading Detection on PV Modules for Precise Power Loss Ratio Estimation Using Digital Image Processing

Challenges of a small world analysis for the continuous monitoring of behavior in mice

Detection and Model of Thermal Traces Left after Aggressive Behavior of Laboratory Rodents

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