Since substations are key parts of power transmission, ensuring the safety of substations involves monitoring whether the substation equipment is in a normal state. Oil leakage detection is one of the necessary daily tasks of substation inspection robots, which can immediately find out whether there is oil leakage in the equipment in operation so as to ensure the service life of the equipment and maintain the safe and stable operation of the system. At present, there are still some challenges in oil leakage detection in substation equipment: there is a lack of a more accurate method of detecting oil leakage in small objects, and there is no combination of intelligent inspection robots to assist substation inspection workers in judging oil leakage accidents. To address these issues, this paper proposes a small object detection method for oil leakage defects in substations. This paper proposes a small object detection method for oil leakage defects in substations, which is based on the feature extraction network Resnet-101 of the Faster-RCNN model for improvement. In order to decrease the loss of information in the original image, especially for small objects, this method is developed by canceling the downsampling operation and replacing the large convolutional kernel with a small convolutional kernel. In addition, the method proposed in this paper is combined with an intelligent inspection robot, and an oil leakage decision-making scheme is designed, which can provide substation equipment oil leakage maintenance recommendations for substation workers to deal with oil leakage accidents. Finally, the experimental validation of real substation oil leakage image collection is carried out by the intelligent inspection robot equipped with a camera. The experimental results show that the proposed FRRNet101-c model in this paper has the best performance for oil leakage detection in substation equipment compared with several baseline models, improving the Mean Average Precision (mAP) by 6.3%, especially in detecting small objects, which has improved by 12%.

Due to the complex environment of the substation, the inspection work of the substation becomes time-consuming and laborious. As a result, the substation inspection robot has gradually become a hot research point. At present, the mainstream intelligent inspection robots for substations use high-precision LiDAR sensors for navigation, which has high navigation accuracy but cannot identify the types of obstacles and the road contour boundary, seriously affecting the inspection performance and efficiency. Meanwhile, the high-precision 3D laser radar is too expensive to afford. In order to solve these problems, a novel navigation assistant method is proposed in this paper, which is based on multisensory information fusion. Asynchronous information matching with multiple sensors was used to match the information collected by different sensors to deal with the time asynchrony. To express the height of obstacles, 2D laser radar was applied to create 3D imaging by being combined with inertial measurement unit (IMU). For perceiving and under-standing the substation environment independently by the inspection robot, ENet was given over to segment color point cloud maps, which was built by introducing optical sensor data. The method was implemented based on the ROS system and transplanted to embedded platform of the inspection robot. Finally, experimental results show that, compared with VLP-32C 3D laser radar sensors, the data volume of navigation assistance module had reduced by 95%. Meanwhile, after training the ENet network, the mean average accuracy value had achieved 86%, which meets the needs of practical engineering applications. In addition, the inspection robot equipped with the navigation assistance module is successfully tested in multiple substations, which shows that the robot can not only identify the road contour and the type of obstacles on the way, but also reduce the amount of data and the cost of hardware.

Compared with traditional manual inspection, inspection robots can not only meet the all-weather, real-time, and accurate inspection needs of substation inspection, they also reduce the work intensity of operation and maintenance personnel and decrease the probability of safety accidents. For the urgent demand of substation inspection robot intelligence enhancement, an environment understanding algorithm is proposed in this paper, which is an improved DeepLab V3+ neural network. The improved neural network replaces the original dilate rate combination in the ASPP (atrous spatial pyramid pooling) module with a new dilate rate combination with better segmentation accuracy of object edges and adds a CBAM (convolutional block attention module) in the two up-samplings, respectively. In order to be transplanted to the embedded platform with limited computing resources, the improved neural network is compressed. Multiple sets of comparative experiments on the standard dataset PASCAL VOC 2012 and the substation dataset have been made. Experimental results show that, compared with the DeepLab V3+, the improved DeepLab V3+ has a mean intersection-over-union (mIoU) of eight categories of 57.65% on the substation dataset, with an improvement of 6.39%, and the model size of 13.9 M, with a decrease of 147.1 M.