Insulator defect detection is of great significance to ensure the normal operation of power transmission and distribution networks. In response to the problems of low speed, low accuracy, and difficulty in deploying to embedded terminals in existing insulator defect detection, this paper proposes a lightweight insulator defect detection model based on an improved YOLOv5s, named LiteYOLO-ID. Firstly, to significantly reduce the model parameters while maintaining detection accuracy, we design a new lightweight convolution module called EGC (ECA-GhostNet-C2f). Secondly, based on the EGC module, we construct the EGC-CSPGhostNet backbone network, which optimizes the feature extraction process and achieves model compression. Additionally, we design a lightweight neck network, EGC-PANet, to further reduce the parameter count and achieve efficient feature fusion. Experimental results show that on the IDID-Plus dataset, compared to the original YOLOv5s model, not only does LiteYOLO-ID reduce the model parameters by 47.13%, but it also improves the mAP(0.5) by 1%. Furthermore, the generalization of the model is validated on the Pascal VOC dataset and the SFID dataset. Importantly, after TensorRT optimization, the inference speed of the LiteYOLO-ID algorithm on the Jetson TX2 NX reaches 20.2 FPS, meeting the real-time detection requirements of insulator defects. Our code, weight models, and datasets can be obtained at the following URL: https://github.com/LuYang-2023/Insulator-defect-detection.

Due to the advantages of safety and economy, it has become a trend to use unmanned aerial vehicles (UAVs) instead of humans to inspect high-voltage transmission lines. Considering the manual inspection process and the few-shot learning, a two-stage method for insulator anomaly detection is proposed. In the first stage, a positioning-restoration-cropping method is discussed for insulator string detection and processing. In the second stage, an insulator anomaly detection model called a multi-scale feature reweighting (MFR) network is built. With the help of few-shot object detection, the detection of five kinds of anomaly insulator caps, such as falling off, breakage and ablation is realized. The mean average precision (mAP) of the proposed method is 88.76%.

In criminal cases, the information collection and analysis of the scene plays an important role in solving cases. Handprint mark can be easily remaining and frequently occurring mark of criminals. It may occasionally be recognized as potential physical evidence during criminal investigations. The infrared thermal imager can fully collect the thermal radiation information of objects, so it is convenient to get the remaining heat source marks in the environment, and to obtain key information that is missing from other methods. Handprints tend to disappear over time. Like cell apoptosis is regulated by a rigorous mechanism of a series of genes, handprints also regulated by the mechanism of mark disappearance affected by surrounding pixels. In fact, we can easily get handprint images which hands left a period of time, but hope to restore the initial handprint image and obtain the characteristics of the criminal hand. Therefore, a method of infrared handprint image restoration based on apoptotic mechanism is proposed. Training depends on the existing infrared handprint images, and we can find a relational model between the former and the latter images. Finally, the rough shape of the original infrared handprint can be deduced.