According to the invention, a body photo, obtained by routing inspection by an unmanned aerial vehicle, of a transmission tower is analyzed automatically by using a deep learning technology. On the basis of a Faster-Rcnn algorithm, power transmission equipment needing detection is identified from the photo obtained by routing inspection by the unmanned aerial vehicle; the power transmission equipment is sent to a defect classifier for the equipment to determine whether the equipment has a defect and which type the defect is; if the equipment has a defect, the location of the defect equipment as well as the defect type is marked in the photo automatically; and then a defect report is generated finally and maintenance information is provided for the maintenance staff. According to the invention, the analysis process has advantages of high accuracy, fast processing speed and high reliability; and the automatic analysis of the inspection photo of the transmission line is realized without manual participation.

Deep-learning-based method for identifying defect of power transmission equipment automatically

The embodiment of the invention discloses a moving target behavior tracking method, a device, a device and a computer-readable storage medium. The method includes invoking the pre-constructed object detection model to detect the object in the moving object image so as to locate the position of the moving object in the moving object image. The target detection model is based on YOLOv3 algorithm andtrained with regularization and binary cross entropy as loss function. The target detection model selects the target boundary box satisfying the preset conditions according to the intersection ratioand non-maximum suppression algorithm, and determines the center coordinate of the target boundary box to locate the position of the moving target. The present application utilizes an object detectionimage model to locate a moving object in a captured moving object image, and for a plurality of successive frames of successive images, by locating a moving object in each frame of the images, tracking detection of a moving object behavior in a video is realized. The present application improves the detection speed and positioning accuracy of a moving object.

Moving object behavior tracking method, device, apparatus, and readable storage medium

The invention discloses a sea surface ship video detecting and tracking method based on deep learning. (1) Convolutional model feature extraction: the features of the target ship are automatically learnt by using a deep convolutional neural network, a series of 3x3 convolution kernels are applied to extract features of each feature layer, the detail features are mainly extracted from the shallow convolution layers, and the abstract global contour features are mainly extracted from the deep convolution layers; (2) multi-scale full convolutional model target positioning and classification: massive features acquired by the trunk convolutional network are utilized to be integrated again on the multi-scale convolutional layer so as to construct the scale decreasing target feature pyramid network, each layer represents different sizes of target features and the feature layers perform fusion comparison; and (3) target tracking of successive frames of full connection: the features of the areanear the target are selected from the current frame and last frame of images through convolutional layer feature extraction, and the features are cascaded and inputted to the full connection layer. The real-time performance and the stability of ship detecting and tracking can be effectively ensured.

Sea surface ship video detecting and tracking method based on deep learning

The invention discloses an optical remote sensing image ship detection method based on a depth learning single-step detector and mainly solves problems of the limited ability of artificially-designedcharacteristic representation in traditional ship detection methods, insufficient interference resistance capability against the complex background and tedious and slow detection process. The method comprises steps that 1), a remote sensing image data set is established; 2), VGG-16 is utilized as a characteristic extraction portion, a multipath residual block is constructed and is taken as a prediction portion, and a new loss function is constructed; 3), the characteristic extraction portion, the prediction portion and the loss function form a single-step detector; 4), the established data setis utilized to train the single-step detector; and 5), ship detection is carried out thorugh utilizing the trained single-step detector. The method is advantaged in that the method is simple for realization, a ship target in the complex background can be rapidly and accurately detected, and the method is applicable to multiple types of real-time ship detection systems.

Optical remote sensing image ship detection method based on depth learning single-step detector

The invention discloses a small target detection method based on feature fusion and depth learning, which solves the problems of poor detection accuracy and real-time performance for small targets. The implementation scheme is as follows: extracting high-resolution feature map through deeper and better network model of ResNet 101; extracting Five successively reduced low resolution feature maps from the auxiliary convolution layer to expand the scale of feature maps. Obtaining The multi-scale feature map by the feature pyramid network. In the structure of feature pyramid network, adopting deconvolution to fuse the feature map information of high-level semantic layer and the feature map information of shallow layer; performing Target prediction using feature maps with different scales and fusion characteristics; adopting A non-maximum value to suppress the scores of multiple predicted borders and categories, so as to obtain the border position and category information of the final target. The invention has the advantages of ensuring high precision of small target detection under the requirement of ensuring real-time detection, can quickly and accurately detect small targets in images, and can be used for real-time detection of targets in aerial photographs of unmanned aerial vehicles.

Small target detection method based on feature fusion and depth learning

The invention discloses a depth learning target detection method based on pre-segmentation and regression, mainly to solve the problems that the existing target detection method is poor in small target detection precision and long in detection time. The method comprises steps: 1) a quadtree segmentation algorithm is used to extract a region of interest in a to-be-detected image; 2) a basic convolution layer and an auxiliary convolution layer are used to carry out feature extraction on the region of interest, and feature graphs of multiple scales are obtained; 3) the position information of a default border is calculated on the feature graphs of multiple scales, a convolution filter is used for detection on the feature graphs of multiple scales, and multiple predicted borders and multiple category scores are obtained; and 4) non-maximum suppression is used for the multiple predicted borders and the multiple category scores, and the final target border position and the category information are obtained. A small target in the image can be quickly and accurately detected, and the method can be used for target real-time detection in unmanned aerial vehicle aerial photographing.

Depth learning target detection method based on pre-segmentation and regression

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Depth learning target detection method based on pre-segmentation and regression