Obstacle Detection in the Operation of Driverless Locomotive Based on ConvNext
Mine electric locomotive is the main equipment for mine transportation.Because of the complex underground operating environment and limited lighting conditions,unmanned electric locomotive is easy to collide with obstacles in the forward direction and cause derailment,thus affecting the operating efficiency of electric locomotive and the normal production of mine.Therefore,it is of great significance to accurately identify the obstacles that threaten the normal running of the locomotive,which is of great significance to improve the operating efficiency of the electric locomotive and ensure the personal safety of the operators.ConvNext model has good feature extraction effect,fast training speed and high detection rate of target detection,which can achieve the expected results.Therefore,this paper attempts to use ConvNext to detect obstacles in the process of electric locomotive driving.The experimental results are the same as exp ected,and the detection accuracy of ConvNext algorithm meets the requirements,which can accurately detect obstacles in various complex environments when locomotives are running,and the mAP can reach 87.5%.
mine electric locomotiveobstacle detectionConvNextdseep learning
NETL
NSTL
万方数据
