A Lightweight Algorithm for Insulator Target Detection and Defect Identification.

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.

Improved Algorithm for Insulator and Its Defect Detection Based on YOLOX.

Aerial insulator defect images have some features. For instance, the complex background and small target of defects would make it difficult to detect insulator defects quickly and accurately. To solve the problem of low accuracy of insulator defect detection, this paper concerns the shortcomings of IoU and the sensitivity of small targets to the model regression accuracy. An improved SIoU loss function was proposed based on the regular influence of regression direction on the accuracy. This loss function can accelerate the convergence of the model and make it achieve better results in regressions. For complex backgrounds, ECA (Efficient Channel Attention Module) is embedded between the backbone and the feature fusion layer of the model to reduce the influence of redundant features on the detection accuracy and make progress in the aspect. As a result, these experiments show that the improved model achieved 97.18% mAP which is 2.74% higher than before, and the detection speed could reach 71 fps. To some extent, it can detect insulator and its defects accurately and in real-time.

Simultaneous and automated detection of influenza A virus hemagglutinin H7 and H9 based on magnetism and size mediated microfluidic chip.

Influenza viruses with multiple subtypes have highly virulent in humans, of which influenza hemagglutinin (HA) is the major viral surface antigen. Simultaneous and automated detection of multiple influenza HA are of great importance for early-stage diagnosis and operator protection. Herein, a magnetism and size mediated microfluidic platform was developed for point-of-care detection of multiple influenza HA. With multiplex microvalves and computer program control, the detection process showed high automation which had a great potential for avoiding the high-risk virus exposure to the operator. Taking advantage of magnetism and size mediated multiple physical fields, multiple influenza HA could be simultaneous separation and detection depended on different-size magnetic beads. Using high-luminance quantum dots as reporter, this assay achieved high sensitivity with a detection limit of 3.4 ng/mL for H7N9 HA and 4.5 ng/mL for H9N2 HA, and showed excellent specificity, anti-interference ability and good reproducibility. These results indicate that this method may propose new avenues for early detection of multiple influenza subtypes.