Locating the tiny insulator defect object with complex backgrounds in high-resolution aerial images is a challenging task. In this paper, we propose a novel method which cascades detection and segmentation networks to identify the defect from the global and local two levels: (1) The improved Faster R-CNN is carried out to capture both defects and insulators in the entire image. ResNeXt-101 is adopted as the feature extraction network so as to fully extract features, and Feature Pyramid Network (FPN) is built to enhance the ability of detecting small targets. In addition, the Online Hard Example Mining (OHEM) training strategy is applied to solve the imbalance problem of positive and negative samples. (2) All the detected insulators are extracted and fed into the improved U-Net network to futher inspect at pixel level, we utilize the pre-trained ResNeXt-50 as the encoder of U-Net, incorporate an attention module, Spatial and Channel Squeeze & Excitation Block (SCSE), into the decoding path to highlight the meaningful information. A hybrid loss which merges binary cross entropy (BCE) loss and dice coefficient loss is designed to train our network for figuring out the class imbalance issue. The missed detection can be greatly reduced with the combination of two modified network, which makes comprehensive use of the original map information and local information. On the test set of actual images, the insulator defect recognition precision and recall of the cascade network is 91.9% and 95.7% , exhibiting strong robustness and accuracy.

This paper presents a nonlinear microwave device modeling technique that is based on time delay neural network (TDNN). The proposed technique can accurately model the nonlinear microwave devices when compared to static neural network modeling method. A new formulation is developed to allow for the proposed TDNN model to be trained with DC, small-signal, and large signal data, which can enhance the generalization of the device model. An algorithm is formulated to train the proposed TDNN model efficiently. This proposed technique is verified by GaAs metal-semiconductor-field-effect transistor (MESFET), and GaAs high-electron mobility transistor (HEMT) examples. These two examples demonstrate that the proposed TDNN is an efficient and valid approach for modeling various types of nonlinear microwave devices.

Small target detection has always been a hot and difficult point in the field of target detection. The existing detection network has a good effect on conventional targets but a poor effect on small target detection. The main challenge is that small targets have few pixels and are widely distributed in the image, so it is difficult to extract effective features, especially in the deeper neural network. A novel plug-in to extract location features of the small target in the deep network was proposed. Because the deep network has a larger receptive field and richer global information, it is easier to establish global spatial context mapping. The plug-in named location feature extraction establishes the spatial context mapping in the deep network to obtain the global information of scattered small targets in the deep feature map. Additionally, the attention mechanism can be used to strengthen attention to the spatial information. The comprehensive effect of the above two can be utilized to realize location feature extraction in the deep network. In order to improve the generalization of the network, a new self-distillation algorithm was designed for pre-training that could work under self-supervision. The experiment was conducted on the public datasets (Pascal VOC and Printed Circuit Board Defect dataset) and the self-made dedicated small target detection dataset, respectively. According to the diagnosis of the false-positive error distribution, the location error was significantly reduced, which proved the effectiveness of the plug-in proposed for location feature extraction. The mAP results can prove that the detection effect of the network applying the location feature extraction strategy is much better than the original network.