Abstract: A lithium battery power display method and corresponding system, the method includes: during charging process of lithium battery, periodically calculating SOC value charged in this charging through a current integration method (S10); obtaining a sum of the SOC value displayed before charging and the SOC value charged, determining whether it is in a predetermined charging platform area (S11); when determination result is in the predetermined charging platform area and it is determined the correction trigger condition is met, a correction coefficient is calculated according to pre-calibrated formula, the SOC value to be displayed is obtained by correcting the SOC value charged with the correction coefficient, the correction coefficient is positive number less than 1 (S12); displaying the SOC value to be displayed (S13). Solve virtual electricity caused by large SOC error in non-full charge and discharge state of lithium batteries, improve experience of lithium battery electric vehicles.

Abstract: A method for detecting road diseases by intelligent cruise via an unmanned aerial vehicle (UAV), the UAV and a detecting system therefor are provided. The method for detecting road diseases by intelligent cruise via UAV, wherein a road disease detection model and a road recognition model based on deep learning network are built in the UAV, wherein the method specifically comprises a step of: automatically flying the UAV on a predetermined route on the actual road determined by the road recognition model, and obtaining road disease test results by the road surface disease detection model. The present invention adopts the road recognition model and road disease detection model based on deep learning network, which can realize automatic cruise and automatic road disease detection, only need to set a predetermined route or area range, which is convenient and fast.

Abstract: The present application discloses a pipeline radar and television inspection robot which includes a robot body, a directional drilling lifting device, a directional drilling rotary device, a directional drilling swing device, a radar, cameras and a driving apparatus; wherein the directional drilling lifting device is on a front part of the robot body; the directional drilling rotary device is on the directional drilling lifting device; the directional drilling swing device is on the directional drilling rotary device; the radar and the cameras are on the directional drilling swing device; the driving apparatus are on a bottom of the robot body. The directional drilling lifting device, the radar and the cameras are plugged in the robot body. The robot body electrically connects to cables which electrically connect to a control system. The cameras and the radar are able to be adjusted and the components are connected as modules.

Abstract: A method for detecting road diseases by intelligent cruise via an unmanned aerial vehicle (UAV), the UAV and a detecting system therefor are provided. The method for detecting road diseases by intelligent cruise via UAV, wherein a road disease detection model and a road recognition model based on deep learning network are built in the UAV, wherein the method specifically comprises a step of: automatically flying the UAV on a predetermined route on the actual road determined by the road recognition model, and obtaining road disease test results by the road surface disease detection model. The present invention adopts the road recognition model and road disease detection model based on deep learning network, which can realize automatic cruise and automatic road disease detection, only need to set a predetermined route or area range, which is convenient and fast.

Abstract: An image segmentation method and system for a pavement disease based on deep learning are provided, relating to a field of image processing. The image segmentation method includes steps of: acquiring a pavement detection image; inputting the pavement detection image into a disease segmentation model which is obtained through training a deep learning network with a disease database; recognizing and segmenting the pavement disease, and obtaining a segmented image of the pavement disease. The image segmentation method adopts a deep learning algorithm for image segmentation, so that a pavement disease region is automatically obtained, a working efficiency is improved and meanwhile image segmentation becomes more accurate.

Abstract: The present application discloses a pipeline radar and television inspection robot which includes a robot body, a directional drilling lifting device, a directional drilling rotary device, a directional drilling swing device, a radar, cameras and a driving apparatus; wherein the directional drilling lifting device is on a front part of the robot body; the directional drilling rotary device is on the directional drilling lifting device; the directional drilling swing device is on the directional drilling rotary device; the radar and the cameras are on the directional drilling swing device; the driving apparatus are on a bottom of the robot body. The directional drilling lifting device, the radar and the cameras are plugged in the robot body. The robot body electrically connects to cables which electrically connect to a control system. The cameras and the radar are able to be adjusted and the components are connected as modules.