Accurate target tracking based on visual images is the key for intelligent robots to assist or replace astronauts to work in space station. However, the special space environment such as non-uniform illumination and h...Accurate target tracking based on visual images is the key for intelligent robots to assist or replace astronauts to work in space station. However, the special space environment such as non-uniform illumination and high-energy particle radiation is a huge challenge, which may lead to complex noise coupling in vision image. This paper proposes a novel method for accurate target tracking, the essence of which is the Retinex image enhancement algorithm in CIELAB color space(LAB-GRetinex) and the generalized maximum correntropy Kalman filter(GMCKF) which are all based on generalized Gaussian distribution. The LABGRetinex algorithm chooses the CIELAB color space, which is closer to the human vision, as the processing color space, and the generalized Gaussian distribution can estimate the light image accurately, so the influence of non-uniform illumination can be reduced effectively. Meanwhile, the GMCKF algorithm adopts the generalized correntropy criterion based on the generalized Gaussian distribution to replace the minimum mean square error(MMSE) criterion to realize the optimal filtering effect under the complex non-Gaussian noise, which can improve the target tracking accuracy. Sufficient ground simulation experiments and application experiments in the Tiangong-2 space laboratory verify the effectiveness of the proposed algorithm which can track the target accurately in the special space environment and provide the precise pose information for on-orbit robot maintenance verification. This research lays a technical foundation for the application of intelligent robot in the construction and operation on space station in the future.展开更多
To realize automatic harvesting of the jujube,the jujube harvester was designed and manufactured.For achieving the jujube harvester autopilot,a novel algorithm for visual navigation path detection was proposed.The cen...To realize automatic harvesting of the jujube,the jujube harvester was designed and manufactured.For achieving the jujube harvester autopilot,a novel algorithm for visual navigation path detection was proposed.The centerline of tree row lines was taken as the navigation path.The method included four main parts:image preprocessing,image segmentation,tree row lines access,and navigation path access.The methods of threshold segmentation,noise removal,and border smoothing were utilized on the image in Lab color space for the image segmentation.The least square method was employed to fit the tree row lines,and the centerline was obtained as the navigation path.Experimental results indicated that the average false detection rate was 3.98%,and the average detection speed was 41 fps.The algorithm meets the requirements of the jujube harvester autopilot in terms of accuracy and speed.It also can lay the foundation for accomplishing the jujube harvester vision-based autopilot.展开更多
Recognition and counting of greenhouse pests are important for monitoring and forecasting pest population dynamics.This study used image processing techniques to recognize and count whiteflies and thrips on a sticky t...Recognition and counting of greenhouse pests are important for monitoring and forecasting pest population dynamics.This study used image processing techniques to recognize and count whiteflies and thrips on a sticky trap located in a greenhouse environment.The digital images of sticky traps were collected using an image-acquisition system under different greenhouse conditions.If a single color space is used,it is difficult to segment the small pests correctly because of the detrimental effects of non-uniform illumination in complex scenarios.Therefore,a method that first segments object pests in two color spaces using the Prewitt operator in I component of the hue-saturation-intensity(HSI)color space and the Canny operator in the B component of the Lab color space was proposed.Then,the segmented results for the two-color spaces were summed and achieved 91.57%segmentation accuracy.Next,because different features of pests contribute differently to the classification of pest species,the study extracted multiple features(e.g.,color and shape features)in different color spaces for each segmented pest region to improve the recognition performance.Twenty decision trees were used to form a strong ensemble learning classifier that used a majority voting mechanism and obtains 95.73%recognition accuracy.The proposed method is a feasible and effective way to process greenhouse pest images.The system accurately recognized and counted pests in sticky trap images captured under real greenhouse conditions.展开更多
基金supported by the Key Program of National Natural Science Foundation of China(Grant Nos.61733001&U1713215)the National Natural Science Foundation of China(Grant Nos.61573063&61873039)
文摘Accurate target tracking based on visual images is the key for intelligent robots to assist or replace astronauts to work in space station. However, the special space environment such as non-uniform illumination and high-energy particle radiation is a huge challenge, which may lead to complex noise coupling in vision image. This paper proposes a novel method for accurate target tracking, the essence of which is the Retinex image enhancement algorithm in CIELAB color space(LAB-GRetinex) and the generalized maximum correntropy Kalman filter(GMCKF) which are all based on generalized Gaussian distribution. The LABGRetinex algorithm chooses the CIELAB color space, which is closer to the human vision, as the processing color space, and the generalized Gaussian distribution can estimate the light image accurately, so the influence of non-uniform illumination can be reduced effectively. Meanwhile, the GMCKF algorithm adopts the generalized correntropy criterion based on the generalized Gaussian distribution to replace the minimum mean square error(MMSE) criterion to realize the optimal filtering effect under the complex non-Gaussian noise, which can improve the target tracking accuracy. Sufficient ground simulation experiments and application experiments in the Tiangong-2 space laboratory verify the effectiveness of the proposed algorithm which can track the target accurately in the special space environment and provide the precise pose information for on-orbit robot maintenance verification. This research lays a technical foundation for the application of intelligent robot in the construction and operation on space station in the future.
基金supported by the National Key R&D Program of China(No.2016YFD0701504).
文摘To realize automatic harvesting of the jujube,the jujube harvester was designed and manufactured.For achieving the jujube harvester autopilot,a novel algorithm for visual navigation path detection was proposed.The centerline of tree row lines was taken as the navigation path.The method included four main parts:image preprocessing,image segmentation,tree row lines access,and navigation path access.The methods of threshold segmentation,noise removal,and border smoothing were utilized on the image in Lab color space for the image segmentation.The least square method was employed to fit the tree row lines,and the centerline was obtained as the navigation path.Experimental results indicated that the average false detection rate was 3.98%,and the average detection speed was 41 fps.The algorithm meets the requirements of the jujube harvester autopilot in terms of accuracy and speed.It also can lay the foundation for accomplishing the jujube harvester vision-based autopilot.
基金This work was financially supported by the National Natural Science Foundation of China(Grant No.61601034)and the National Natural Science Foundation of China(Grant No.31871525)The authors acknowledge Kimberly Moravec,PhD,from Liwen Bianji,Edanz Editing China(www.liwenbianji.cn/ac),for editing the English text of a draft of this manuscript.
文摘Recognition and counting of greenhouse pests are important for monitoring and forecasting pest population dynamics.This study used image processing techniques to recognize and count whiteflies and thrips on a sticky trap located in a greenhouse environment.The digital images of sticky traps were collected using an image-acquisition system under different greenhouse conditions.If a single color space is used,it is difficult to segment the small pests correctly because of the detrimental effects of non-uniform illumination in complex scenarios.Therefore,a method that first segments object pests in two color spaces using the Prewitt operator in I component of the hue-saturation-intensity(HSI)color space and the Canny operator in the B component of the Lab color space was proposed.Then,the segmented results for the two-color spaces were summed and achieved 91.57%segmentation accuracy.Next,because different features of pests contribute differently to the classification of pest species,the study extracted multiple features(e.g.,color and shape features)in different color spaces for each segmented pest region to improve the recognition performance.Twenty decision trees were used to form a strong ensemble learning classifier that used a majority voting mechanism and obtains 95.73%recognition accuracy.The proposed method is a feasible and effective way to process greenhouse pest images.The system accurately recognized and counted pests in sticky trap images captured under real greenhouse conditions.
