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To further improve the accuracy and speed of UAV inspection of transmission line insulator defects, this paper proposes an insulator detection and defect identification algorithm based on YOLOv4, which is called DSMH-YOLOv4. In the feature extraction network of the YOLOv4 model, the improved algorithm improves the residual edges of the residual structure based on feature reuse and designs the backbone network D-CSPDarknet53, which greatly reduces the number of parameters and computation of the model. The SA-Net (Shuffle Attention Neural Networks) attention model is embedded in the feature fusion network to strengthen the attention of target features and improve the weight of the target. Multi-head output is added to the output layer to improve the ability of the model to recognize the small target of insulator damage. The experimental results show that the number of parameters of the improved algorithm model is only 25.98% of that of the original model, and the mAP (mean Average Precision) of the insulator and defect is increased from 92.44% to 96.14%, which provides an effective way for the implementation of edge end algorithm deployment. [ABSTRACT FROM AUTHOR]

The accuracy of insulators and their defect identification by UAVs (unmanned aerial vehicles) in transmission-line inspection needs to be further improved, and the model size of the detection algorithm is significantly reduced to make it more suitable for edge-end deployment. In this paper, the algorithm uses a lightweight GhostNet module to reconstruct the backbone feature extraction network of the YOLOv4 model and employs depthwise separable convolution in the feature fusion layer. The model is lighter on the premise of ensuring the effect of image information extraction. Meanwhile, the ECA-Net channel attention mechanism is embedded into the feature extraction layer and PANet (Path Aggregation Network) to improve the recognition accuracy of the model for small targets. The experimental results show that the size of the improved model is reduced from 244 MB to 42 MB, which is only 17.3% of the original model. At the same time, the mAp of the improved model is 0.77% higher than that of the original model, reaching 95.4%. Moreover, the mAP compared with YOLOv5-s and YOLOX-s, respectively, is improved by 1.98% and 1.29%. Finally, the improved model is deployed into Jetson Xavier NX and run at a speed of 8.8 FPS, which is 4.3 FPS faster than the original model. [ABSTRACT FROM AUTHOR]

• A new operational model with ant-miner algorithm was proposed to predict the risk of forest fires in cloud-rich areas. • The performance of proposed model was compared with that of the national risk prediction model of China. • The proposed model was compared to other risk prediction models with two classical machine learning algorithms. • The ant-miner algorithm-based model was more effective and reliable. Annually, millions of hectares of forest lands around the world are destroyed by fires. To minimize the fire-caused losses, more studies on the risk prediction of forest fires need to be carried out. For predicting the risk of forest fires in cloud-rich areas (e.g., the southwest of China), the synergetic use of operational forecasting systems and remote sensing-based models is expected to have a consistent performance. Therefore, we proposed in this study a new model based on ant-miner algorithm which has a good capability of solving multivariable and non-linear problems in the synergetic modeling of multi-source data. Based on historical fire data during 2000–2018 in Chongqing city, its performance was tested, and then was compared with that of other three models (i.e., meteorological data-, Artificial Neural Network-, and Support Vector Machine-based models). Results showed that, without interference from human factors, the risk predictions of proposed model were more objective. And, its mined-rules were easier to understand and also portable across multiple GIS platforms. Moreover, the proposed model has a better performance at predicting risk levels (i.e., overall accuracy was 79.02% and Kappa coefficient was 0.678) and the spatial distribution of its predictions were more detailed. This research indicated that the ant-miner algorithm-based model was more effective and reliable, and it could be used for constructing the operational system of risk predictions for forest fires in cloud-rich areas. [ABSTRACT FROM AUTHOR]