Sensors, Vol. 23, Pages 9482: Dynamic and Real-Time Object Detection Based on Deep Learning for Home Service Robots

Sensors, Vol. 23, Pages 9482: Dynamic and Real-Time Object Detection Based on Deep Learning for Home Service Robots Sensors doi: 10.3390/s23239482 Authors: Yangqing Ye Xiaolon Ma Xuanyi Zhou Guanjun Bao Weiwei Wan Shibo Cai Home service robots operating indoors, such as inside houses and offices, require the real-time and accurate identification and location of target objects to perform service tasks efficiently. However, images captured by visual sensors while in motion states usually contain varying degrees of blurriness, presenting a significant challenge for object detection. In particular, daily life scenes contain small objects like fruits and tableware, which are often occluded, further complicating object recognition and positioning. A dynamic and real-time object detection algorithm is proposed for home service robots. This is composed of an image deblurring algorithm and an object detection algorithm. To improve the clarity of motion-blurred images, the DA-Multi-DCGAN algorithm is proposed. It comprises an embedded dynamic adjustment mechanism and a multimodal multiscale fusion structure based on robot motion and surrounding environmental information, enabling the deblurring processing of images that are captured under different motion states. Compared with DeblurGAN, DA-Multi-DCGAN had a 5.07 improvement in Peak Signal-to-Noise Ratio (PSNR) and a 0.022 improvement in Structural Similarity (SSIM). An AT-LI-YOLO method is proposed for small and occl...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research