Hand Detection and Location Based on Improved SSD for Space Human-Robot Interaction

Gao, Qing; Liu, Jinguo; Ju, Zhaojie; Zhang, Lu; Li, Yangmin; Liu, Yuwang

doi:10.1007/978-3-319-97586-3_15

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Which have shown outstanding performance in the field of object detection. Lots of previous work improve these approaches for gesture recognition and get good applications [31]- [36]. This section briefly reviews existing works belonging to two major categories: two-stage detector and one-stage detector.…”

Section: B Object Detection Methods Based On Deep Learning and Lightmentioning

confidence: 99%

Hierarchical Attention-Based Astronaut Gesture Recognition: A Dataset and CNN Model

Zhang

Wang

2020

IEEE Access

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In the space station, visual gesture recognition is an important component for gesture based human-robot interaction to control the robot, which assists astronauts to complete simple, repetitive and cooperative work. However, public gesture datasets are captured in the daily environment and existing approaches trained on these datasets achieve imprecise performance for astronaut gesture recognition. In this paper, we introduce a new astronaut gesture dataset (DSSL-Astronaut gesture dataset) and a novel hierarchical attention single-shot detector network (HA-SSD) for astronaut gesture recognition. Specifically, this dataset consists of the real and augmented images. The real images captured in the simulated space station are closed to the real images in the space station environment. Meanwhile, we utilize the Mask-RCNN model to segment the foreground image including an astronaut from real data and capture the background images of the simulated space station at different views and illuminations. Then we combine them to synthesize the augmented images. A novel HA-SSD model consists of a lightweight backbone named MobileNet and a hierarchical attention mechanism. The MobileNet is used as the feature extractor, which contains several depth-wise separable convolutions to make a trade-off between latency and accuracy. Meanwhile, the hierarchical channel-wise attention module is adopted to exploit the fine semantic information to enrich the features for improving the performance of gesture recognition. We conduct experiments to demonstrate that our dataset is suitable for the space station and our approach is able to effectively localize and recognize the gestures with strong generality.

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Section: B Object Detection Methods Based On Deep Learning and Lightmentioning

confidence: 99%

Hierarchical Attention-Based Astronaut Gesture Recognition: A Dataset and CNN Model

Zhang

Wang

2020

IEEE Access

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“…Today, with the development of computer hardware and the advent of deep learning, researchers have become equipped with novel tools, the most prominent of which are various convolutional networks (CNN). There have been many published CNN-based researches on hand detection such as YOLOv1 [ 24 ], YOLOv2 [ 25 ], YOLOv3 [ 26 ], YOLOv4 [ 27 ], YOLOv5 [ 14 , 28 ], YOLOv7 [ 9 ], Mask R-CNN [ 29 , 30 ], SSD [ 31 ], MobileNetv3 [ 32 ], etc. Some of the most prominent results are shown in Figure 1 of Wang et al’s work [ 9 ], where YOLOv7 achieved the best results in terms of accuracy and speed.…”

Section: Related Workmentioning

confidence: 99%

YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos

Nguyen¹,

Nguyen²,

Scherer

et al. 2023

Sensors

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Hand detection and classification is a very important pre-processing step in building applications based on three-dimensional (3D) hand pose estimation and hand activity recognition. To automatically limit the hand data area on egocentric vision (EV) datasets, especially to see the development and performance of the “You Only Live Once” (YOLO) network over the past seven years, we propose a study comparing the efficiency of hand detection and classification based on the YOLO-family networks. This study is based on the following problems: (1) systematizing all architectures, advantages, and disadvantages of YOLO-family networks from version (v)1 to v7; (2) preparing ground-truth data for pre-trained models and evaluation models of hand detection and classification on EV datasets (FPHAB, HOI4D, RehabHand); (3) fine-tuning the hand detection and classification model based on the YOLO-family networks, hand detection, and classification evaluation on the EV datasets. Hand detection and classification results on the YOLOv7 network and its variations were the best across all three datasets. The results of the YOLOv7-w6 network are as follows: FPHAB is P = 97% with TheshIOU = 0.5; HOI4D is P = 95% with TheshIOU = 0.5; RehabHand is larger than 95% with TheshIOU = 0.5; the processing speed of YOLOv7-w6 is 60 fps with a resolution of 1280 × 1280 pixels and that of YOLOv7 is 133 fps with a resolution of 640 × 640 pixels.

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Research on Static Vision-Based Target Localization for Astronaut Assistant Robots

et al. 2019

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Hand Detection and Location Based on Improved SSD for Space Human-Robot Interaction

Cited by 4 publications

References 15 publications

Hierarchical Attention-Based Astronaut Gesture Recognition: A Dataset and CNN Model

Hierarchical Attention-Based Astronaut Gesture Recognition: A Dataset and CNN Model

YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos

Research on Static Vision-Based Target Localization for Astronaut Assistant Robots

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