Classification of House Categories Using Convolutional Neural Networks (CNN)

Viratkapan, Vichai; Mruetusatorn, Saprangsit

doi:10.1109/icbir54589.2022.9786481

Cited by 3 publications

(1 citation statement)

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“…These papers have discussed that CNNs can achieve the application of image classification and recognition by analyzing images with shape and motion characteristics or directly relying on the self-identification ability of the neural network model. Garbage classification [ 26 ], cloud classification [ 27 ], clothing classification [ 28 ], fruit quality classification [ 29 ], and housing classification [ 30 ] are the application examples. In addition, a CNN-based hybrid tracking algorithm was developed to robustly track multiple high-speed moving objects simultaneously in [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

CNN-Based QR Code Reading of Package for Unmanned Aerial Vehicle

Yang

Jan

Chen

et al. 2023

Sensors

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This paper plans to establish a warehouse management system based on an unmanned aerial vehicle (UAV) to scan the QR codes printed on packages. This UAV consists of a positive cross quadcopter drone and a variety of sensors and components, such as flight controllers, single-board computers, optical flow sensors, ultrasonic sensors and cameras, etc. The UAV stabilizes itself by proportional-integral-derivative (PID) control and takes pictures of the package as it reaches ahead of the shelf. Through convolutional neural networks (CNNs), the placement angle of the package can be accurately identified. Some optimization functions are applied to compare system performance. When the angle is 90°, that is, the package is placed normally and correctly, the QR code will be read directly. Otherwise, image processing techniques that include Sobel edge computing, minimum circumscribed rectangle, perspective transformation, and image enhancement is required to assist in reading the QR code. The experimental results showed that the proposed algorithm provided good performance of a recognition rate of 94% for the stochastic gradient descent (SGD) and 95% for Adadelta optimization functions. After that, successful QR code reading was presented.

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