The comparison of Faster R-CNN and Atrous Faster R-CNN in different distance and light condition

Srijakkot, Krit; Kanjanasurat, Isoon; Wiriyakrieng, Nuttakan; Benjangkaprasert, C.

doi:10.1088/1742-6596/1457/1/012015

Cited by 3 publications

(3 citation statements)

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“…Three resolutions and two fps variants were tested, 640x480, 800x600, and 1024x768, with 15 fps and 30 fps, respectively. The three resolutions were chosen because they are standard resolutions for image processing [22], [23]. The two fps variants in the test scenario were selected because 15 fps and 30 fps are common fps choices in monitoring systems [24] and in object detection research [25].…”

Section: The System Implementation and Testingmentioning

confidence: 99%

The Development of Chicken Coop Automatic Remote Visual Monitoring System

Wahjuni

Sanjiwo²,

Wulandari³

et al. 2022

SJI

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Purpose: A remote visual monitoring system will be very helpful for chicken farmers to monitor their cages, that usually located away from their houses. This system needs adequate bandwidth in transmitting the video over the internet, which is usually very limited in urban areas. The main goal of this research is to develop an automatic chicken coop remote monitoring system and define the optimum video resolution to be transmitted. Methods: We used an 8 MP Raspberry Pi camera V2 to record the video and send the results to Google Drive by utilizing the GDrive API. Furthermore, a live streaming video from the chicken coop is accessible through a simple HTTP web page utilizing ngrok as a tunneling software so that the live streaming video can be publicly accessed from anywhere using a web browser. Three video resolutions of 640x480, 800x600, 1024x768 with 15 and 30 framerates were used in our experiments. Each scenario has a duration of five minutes and takes 12 times.Result: The experiment results showed, resolutions that provide a stable video recording and streaming are 640x480 and 800x600. The resulting system succeeded in performing live streaming along with the process of data acquisition. Value: The Google Drive infrastructure is used because of its popularity and convenience by people with limited digital literacy such as smallholder chicken farmers. Furthermore, the video produced by this system can be used in supporting research of chicken behavior pattern identification to build a system notification of an emergency situation in the cage.

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Section: The System Implementation and Testingmentioning

confidence: 99%

The Development of Chicken Coop Automatic Remote Visual Monitoring System

Wahjuni

Sanjiwo²,

Wulandari³

et al. 2022

SJI

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“…Machine learning (ML) is a useful tool for data visualization and management that can quickly and accurately solve a variety of problems. In previous ML research, K. Srijakkot, et al demonstrated the good performance of intruder detection in power substations with different environments and models, including a short calculation time and high accuracy [4,5]. Not only does ML has advantages in detecting intruders, but also in the medical field, where pre-processing and the IterNet model have demonstrated high accuracy in extracting retinal blood vessels [6].…”

Section: Introductionmentioning

confidence: 99%

Comparison of logistic regression and random forest algorithms for airport’s runway assignment

Kanjanasurat

Jungsuwadee

Lasakul

et al. 2023

J. Phys.: Conf. Ser.

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Various automation systems are currently developed using machine learning techniques. It is used to predict and decide on numerous complex tasks in order to reduce the likelihood of human error. Logistic regression is one of the most widely employed machine learning (ML) algorithm. In this study, the accuracy of logistic regression was compared to that of random forest for the assignment of Suvarnabhumi Airport runways to arriving aircraft. The accuracy of the logistic regression model was determined to be 82%, while the accuracy of the random forest model was 77%. Logistic regression was found to be more precise for predicting the appropriate runway to assign to arriving aircraft.

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“…This paper presented the various research issues which can be useful for the researchers to accomplish further research on crack detection. We can see from many authors used several techniques like Artificial Intelligence [7], Image Processing, Machine learning [8] or Deep learning [10] to find the cracking on the surface. However, similar works were done [9] by Elster et.…”

Section: Introductionmentioning

confidence: 99%

Crack Detection of Eggshell using Image Processing and Computer Vision

Purahong

Chaowalittawin

Krungseanmuang

et al. 2022

J. Phys.: Conf. Ser.

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This article presents an eggshell crack inspection using image processing techniques. This approach uses the concept of industrial 4.0 to reduce manual coordination in the egg industry’s manufacturing process. The method started with receiving images from a webcam camera. Then, we rescaled the image to 1147 x 633 for faster computation. Next, divide the image into the red and green channels. The red channel image was converted to grayscale using a Gaussian blur filter with a kernel filter 11 x 11 to reduce noise, followed by turning the image to binary. After that, multiply the binary image with the grayscale of the green channel to remove the background. By that time, a morphological operation was used to enhance the quality of the image. Finally, use the contour matrix to find the area of the object and then build the condition to detect the crack in the eggshell. These techniques of image processing are used to inspect the eggshell crack with a high accuracy of more than 98% as well as the high performance of computing.

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The comparison of Faster R-CNN and Atrous Faster R-CNN in different distance and light condition

Cited by 3 publications

References 1 publication

The Development of Chicken Coop Automatic Remote Visual Monitoring System

The Development of Chicken Coop Automatic Remote Visual Monitoring System

Comparison of logistic regression and random forest algorithms for airport’s runway assignment

Crack Detection of Eggshell using Image Processing and Computer Vision

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