scholarly journals Hardware Detection Method of Transmission Line Patrol Inspection Image Based on Improved YOLOV4 Model

2020 ◽  
Author(s):  
Ning Wang ◽  
Hanghang Zhao ◽  
Wulue Zheng ◽  
Chaoshuo Wang
Keyword(s):  
Fault Diagnosis ◽  
Target Detection ◽  
Performance Index ◽  
Detection Method ◽  
Experimental Results ◽  
Mean Average Precision ◽  
Aerial Images ◽  
State Detection ◽  
K 12 ◽  
Detection Effect

In order to solve the problem of intelligent hardware detection in aerial images, a hardware target detection method based on improved YOLOV4 model is proposed. In order to solve the problems of dense hardware and occlusion in aerial images, the improved network based on channel and spatial hybrid attention mechanism can further improve the detection effect of dense occlusion hardware and reduce image false detection and missed detection. In order to solve the problem that there is a great error in the position of the detection frame caused by the interference between the hardware and the hardware and between the hardware and the background, the prior frame is optimized by K-means++, and it is determined that the anchors generated by K=12 is the best, and the detection boxes are more suitable for the target. The experimental results show that the proposed method solves the problems of missing detection, misdetection and inaccurate detection frame to some extent, in which the mAP (mean Average Precision) value of the performance index is increased from 65.03% to 70.72%. The research can lay a good foundation for further state detection and fault diagnosis of typical hardware.

scholarly journals Insulator Detection Method in Inspection Image Based on Improved Faster R-CNN

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1204 ◽  
Author(s):  
Zhenbing Zhao ◽  
Zhen Zhen ◽  
Lei Zhang ◽  
Yincheng Qi ◽  
Yinghui Kong ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Transmission ◽  
Detection Method ◽  
Fine Tuning ◽  
Average Precision ◽  
Aspect Ratios ◽  
State Detection ◽  
Ap Value ◽  
Detection Effect

The detection of insulators in power transmission and transformation inspection images is the basis for insulator state detection and fault diagnosis in thereafter. Aiming at the detection of insulators with different aspect ratios and scales and ones with mutual occlusion, a method of insulator inspection image based on the improved faster region-convolutional neural network (R-CNN) is put forward in this paper. By constructing a power transmission and transformation insulation equipment detection dataset and fine-tuning the faster R-CNN model, the anchor generation method and non-maximum suppression (NMS) in the region proposal network (RPN) of the faster R-CNN model were improved, thus realizing a better detection of insulators. The experimental results show that the average precision (AP) value of the faster R-CNN model was increased to 0.818 with the improved anchor generation method under the VGG-16 Net. In addition, the detection effect of different aspect ratios and different scales of insulators in the inspection images was improved significantly, and the occlusion of insulators could be effectively distinguished and detected using the improved NMS.

scholarly journals Background Information Self-Learning Based Hyperspectral Target Detection

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Yufei Tian ◽  
Jihai Yang ◽  
Shijun Li ◽  
Wenning Xu
Keyword(s):  
Information Extraction ◽  
Hyperspectral Imaging ◽  
Target Detection ◽  
Detection Method ◽  
Experimental Results ◽  
Hyperspectral Images ◽  
Background Information ◽  
Spectral Information ◽  
Tumor Region ◽  
Self Learning

Hyperspectral imaging has been proved as an effective way to explore the useful information behind the land objects. And it can also be adopted for biologic information extraction, by which the origin information can be acquired from the image repeatedly without contamination. In this paper we proposed a target detection method based on background self-learning to extract the biologic information from the hyperspectral images. The conventional unstructured target detectors are very difficult to estimate the background statistics accurately in either a global or local way. Considering the spatial spectral information, its performance can be further improved by avoiding the above problem. It is especially designed to extract fingerprint and tumor region from hyperspectral biologic images. The experimental results show the validity and the superiority of our method on detecting the biologic information from hyperspectral images.

scholarly journals Vehicle Detection in Very-High-Resolution Remote Sensing Images Based on an Anchor-Free Detection Model with a More Precise Foveal Area

2021 ◽  
Vol 10 (8) ◽  
pp. 549
Author(s):  
Xungen Li ◽  
Feifei Men ◽  
Shuaishuai Lv ◽  
Xiao Jiang ◽  
Mian Pan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Target Detection ◽  
Detection Method ◽  
Vehicle Detection ◽  
Aerial Images ◽  
Background Information ◽  
Remote Sensing Images ◽  
Feature Pyramid ◽  
Very High

Vehicle detection in aerial images is a challenging task. The complexity of the background information and the redundancy of the detection area are the main obstacles that limit the successful operation of vehicle detection based on anchors in very-high-resolution (VHR) remote sensing images. In this paper, an anchor-free target detection method is proposed to solve the problems above. First, a multi-attention feature pyramid network (MA-FPN) was designed to address the influence of noise and background information on vehicle target detection by fusing attention information in the feature pyramid network (FPN) structure. Second, a more precise foveal area (MPFA) is proposed to provide better ground truth for the anchor-free method by determining a more accurate positive sample selection area. The proposed anchor-free model with MA-FPN and MPFA can predict vehicles accurately and quickly in VHR remote sensing images through direct regression and predict the pixels in the feature map. A detailed evaluation based on remote sensing image (RSI) and vehicle detection in aerial imagery (VEDAI) data sets for vehicle detection shows that our detection method performs well, the network is simple, and the detection is fast.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

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