The accurate detection and identification of tea leaf diseases are conducive to its precise prevention and control. Convolutional neural network (CNN) can automatically extract the features of diseased tea leaves in the images. However, tea leaf images taken in natural environments have problems, such as complex backgrounds, dense leaves, and large-scale changes. The existing CNNs have low accuracy in detecting and identifying tea leaf diseases. This study proposes an improved RetinaNet target detection and identification network, AX-RetinaNet, which is used for the automatic detection and identification of tea leaf diseases in natural scene images. AX-RetinaNet uses an improved multiscale feature fusion module of the X-module and adds a channel attention module, Attention. The feature fusion module of the X-module obtains feature maps with rich information through multiple fusions of multi-scale features. The attention module assigns a network adaptively optimized weight to each feature map channel so that the network can select more effective features and reduce the interference of redundant features. This study also uses data augmentation methods to solve the problem of insufficient samples. Experimental results show the detection and identification accuracy of AX-RetinaNet for tea leaf diseases in natural scene images is better than the existing target detection and identification networks, such as SSD, RetinaNet, YOLO-v3, YOLO-v4, Centernet, M2det, and EfficientNet. The AX-RetinaNet detection and identification results indicated the mAP value of 93.83% and the F1-score value of 0.954. Compared with the original network, the mAP value, recall value, and identification accuracy increased by nearly 4%, by 4%, and by nearly 1.5%, respectively.

The accurate detection and timely replacement of abnormal vibration dampers on transmission lines are critical for the safe and stable operation of power systems. Recently, unmanned aerial vehicles (UAVs) have become widely used to inspect transmission lines. In this paper, we constructed a data set of abnormal vibration dampers (DAVDs) on transmission lines in images obtained by UAVs. There are four types of vibration dampers in this data set, and each vibration damper may be rusty, defective, or normal. The challenges in the detection of abnormal vibration dampers on transmission lines in the images captured by UAVs were as following: the images had a high resolution as well as the objects of vibration dampers were relatively small and sparsely distributed, and the backgrounds of cross stage partial networks of the images were complex due to the fact that the transmission lines were erected in a variety of outdoor environments. Existing methods of ground-based object detection significantly reduced the accuracy when dealing with complex backgrounds and small objects of abnormal vibration dampers detection. To address these issues, we proposed an end-to-end parallel mixed attention You Only Look Once (PMA-YOLO) network to improve the detection performance for abnormal vibration dampers. The parallel mixed attention (PMA) module was introduced and integrated into the YOLOv4 network. This module combines a channel attention block and a spatial attention block, and the convolution results of the input feature maps in parallel, allowing the network to pay more attention to critical regions of abnormal vibration dampers in complex background images. Meanwhile, in view of the problem that abnormal vibration dampers are prone to missing detections, we analyzed the scale and ratio of the ground truth boxes and used the K-means algorithm to re-cluster new anchors for abnormal vibration dampers in images. In addition, we introduced a multi-stage transfer learning strategy to improve the efficiency of the original training method and prevent overfitting by the network. The experimental results showed that the $mAP@0.5$ for PMA-YOLO in the detection of abnormal vibration dampers reached 93.8% on the test set of DAVD, 3.5% higher than that of YOLOv4. When the multi-stage transfer learning strategy was used, the $mAP@0.5$ was improved by a further 0.2%.

The existence of clouds affects the interpretation and utilization of remote sensing images. A thin cloud removal algorithm for cloud-contaminated remote sensing images is proposed by combining a multidirectional dual tree complex wavelet transform (M-DTCWT) with domain adaptation transfer least square support vector regression (T-LSSVR). First, M-DTCWT is constructed by using the hourglass filter bank in combination with DTCWT, which is used to decompose remote sensing images into multiscale and multidirectional subbands. Then the low-frequency subband coefficients of the cloud-free regions on target images and source domain images are used as samples for a T-LSSVR model, which can be used to predict those of the cloud regions on cloud-contaminated images. Finally, by enhancing the high-frequency coefficients and replacing the low-frequency coefficients, the thin clouds on cloud-contaminated images are removed. Experimental results show that M-DTCWT contributes to keeping the details of the ground objects of cloud-contaminated images, and the T-LSSVR model can effectively learn the contour information from multisource and multitemporal images, therefore, the proposed method achieves a good effect of thin cloud removal.