CNN-based image small-angle rotation angle estimation

Chen, Dianze; Song, Zhonghao; Guo, Jingyu; Yang, Shuai; Yao, Heng

doi:10.1109/mmsp55362.2022.9949127

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…A new bearing fault diagnosis method based on joint distribution adaptive and deep belief network with improved sparrow search algorithm was proposed by Zhao et al [ 20 ] to effectively solve the learning problem of an inconsistent distribution of training data and test data. Moreover, many papers [ 21 , 22 , 23 , 24 , 25 ] have pointed out that convolutional neural networks (CNNs) can solve different application problems in the application of image classification. 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.…”

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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