Abstract
The accuracy of object detection based on kitchen appliance scene images can suffer severely from external disturbances such as various levels of specular reflection, uneven lighting, and spurious lighting, as well as internal scene-related disturbances such as invalid edges and pattern information unrelated to the object of interest. The present study addresses these unique challenges by proposing an object detection method based on improved faster R-CNN algorithm. The improved method can identify object regions scattered in various areas of complex appliance scenes quickly and automatically. In this paper, we put forward a feature enhancement framework, named deeper region proposal network (D-RPN). In D-RPN, a feature enhancement module is designed to more effectively extract feature information of an object on kitchen appliance scene. Then, we reconstruct a U-shaped network structure using a series of feature enhancement modules. We have evaluated the proposed D-RPN on the dataset we created. It includes all kinds of kitchen appliance control panels captured in nature scene by image collector. In our experiments, the best-performing object detection method obtained a mean average precision mAP value of 89.84% in the testing dataset. The test results show that the proposed improved algorithm achieves higher detecting accuracy than state-of-the-art object detection methods. Finally, our proposed detection method can further be used in text recognition.
Highlights
Object detection is a fundamental issue in the field of computer vision and image processing and has been a hotspot of theoretical and applied research in recent years, with a wide range of applications. e main goal of object detection is to precisely predict the class and location information of various targets in an image or image sequence.e traditional target detection algorithm relies more on manually designed features
Object detection based on kitchen appliance scene images often faces different types and degrees of uncertainty interference, which seriously affects the accuracy of object detection
The position spacing and aspect ratio between different object regions is not a fixed value. e proposed improved object detection methods aim to identify object regions scattered in various areas of complex appliance scenes with uncertain position spacing and aspect ratio quickly and automatically
Summary
Object detection is a fundamental issue in the field of computer vision and image processing and has been a hotspot of theoretical and applied research in recent years, with a wide range of applications. e main goal of object detection is to precisely predict the class and location information of various targets in an image or image sequence. With the rapid development of deep learning, deep learning-based target detection algorithms have proposed solutions to extract image features by using convolution neural networks. E proposed improved object detection methods aim to identify object regions scattered in various areas of complex appliance scenes with uncertain position spacing and aspect ratio quickly and automatically. We replace the original RPN structure by concatenating multiple feature enhancement module in a U-shaped network structure. In this way, it deepens the depth of the network, extracts deeper features, and learns more parameters.
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