Abstract: Depth images have become an important data source for human bone keypoint detection due to their three-dimensional information. To optimize the efficiency of keypoint detection in depth images, a depth image keypoint detection model that combines cascaded depth separable convolution modules is constructed. The model first performs data cleaning and preprocessing on the image, replacing traditional convolutional layers with depthwise separable convolutional modules. The Faster OpenPose network is introduced to replace the traditional convolutional network structure with the lighter MobileNetV1 for detecting keypoints in the image. When the dataset size was 4000, the Faster OpenPose model had an accuracy of 0.97 and a mean square error of 0.03. The recognition rates for four different images were 0.91, 0.87, 0.89, and 0.93, respectively. The processing times were 0.32, 0.31, 0.28, and 0.27, respectively. The method of depth image keypoint detection combined with cascaded depth separable convolution modules has good practicality and excellent detection performance for various images, providing new ideas for future keypoint detection technology research.

Rui Deng, “Deep Image Keypoint Detection Using Cascaded Depth Separable Convolution Modules” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01511101

@article{Deng2024,
title = {Deep Image Keypoint Detection Using Cascaded Depth Separable Convolution Modules},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01511101},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01511101},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Rui Deng}
}