AGV dynamic targets detection method based on improved SSD-MobileNet algorithm
In order to improve the accuracy and frame rate of dynamic obstacle visual detection for automated guided vehicle(AGV),an improved algorithm based on the single shot multibox detector(SSD)was proposed.The lightweight MobileNet network was introduced into the SSD network structure,and then the K-means algorithm was used to cluster and update the Aspect Ratio(AR)values of the real boxes in the training dataset.Finally,an experimental AGV system was built on the embedded Jeston Nano platform and the TensorRT acceleration engine was introduced to optimize the SSD-MobileNet algorithm before and after improvement,and then comparative analysis was made.Experimental results show that the improved SSD-MobileNet algorithm has a mean Average Precision(mAP)value of 79.1%on the AGV using the TensorRT acceleration engine,an increase of 10.8%compared to that before optimization,with little impact on accuracy.The FPS frame rate reaches 25 frames per second,which is 4 times higher than that with the original SSD algorithm,and the model size after improvement is also 37%smaller than that before optimization.The improved algorithm can enable the AGV to complete dynamic obstacle detection tasks during transportation,which can replace the manual transport of objects efficiently and save the transportation cost,and provides a new idea for intelligent transportation.
万方数据
维普
