Target detection plays a key role in the safe driving of autonomous vehicles. At present, most studies use single sensor to collect obstacle information, but single sensor cannot deal with the complex urban road environment, and the rate of missed detection is high. Therefore, this paper presents a detection fusion system with integrating LiDAR and color camera. Based on the original You Only Look Once (YOLO) algorithm, the second detection scheme is proposed to improve the YOLO algorithm for dim targets such as non-motorized vehicles and pedestrians. Many image samples are used to train the YOLO algorithm to obtain the relevant parameters and establish the target detection model. Then, the decision level fusion of sensors is introduced to fuse the color image and the depth image to improve the accuracy of the target detection. Finally, the test samples are used to verify the decision level fusion. The results show that the improved YOLO algorithm and decision level fusion have high accuracy of target detection, can meet the need of real-time, and can reduce the rate of missed detection of dim targets such as non-motor vehicles and pedestrians. Thus, the method in this paper, under the premise of considering accuracy and real-time, has better performance and larger application prospect.
Thermal deformation in machine tools is one of the most significant causes of machining errors. A new approach to predict the thermal error of machine tool is proposed. The temperature variables and the thermal errors are measured using the Pt-100 thermal resistances and eddy current sensors respectively. Fuzzy c-means clustering method is conducted to identify the temperatures, and the representative as an independent variable are selected meanwhile it eliminates the coupling among the variables. The learning and prediction of the thermal errors is achieved using minimal-resource allocating networks by treating the issue as functional mapping between the thermal shifts and the temperature variables. The network is made to predict the error map of a machining center. A traditional radial basis function model is introduced for comparison. The experiment result shows that the fuzzy cmeans clustering method and minimal-resource allocating networks combination is a fast and accurate method for thermal error compensation in machine tools.
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