Multi-Equipment Detection Method for Distribution Lines Based on Improved YOLOx-s

The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.

Similarity evaluation model for the internal defect detection of strip steel

An internal defect meter is an instrument to detect the internal inclusion defects of cold-rolled strip steel.The detection accuracy of the equipment can be evaluated based on the similarity of the multiple detection data obtained for the same steel coil.Based on the cosine similarity model and eigenvalue matrix model,a comprehensive evaluation method to calculate the weighted average of similarity is proposed.Results show that the new method is consistent with and can even replace artificial evaluation to realize the automatic evaluation of strip defect detection results.

An Automated Detection Approach of Protective Equipment Donning for Medical Staff under COVID-19 Using Deep Learning

Personal protective equipment(PPE)donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19.However,the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers’PPE use in the field of healthcare.In this paper,we construct a dress codes dataset for medical staff under the epidemic.And based on this,we propose a PPE donning automatic detection approach using deep learning.With the participation of health care personnel,we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment,and an effective and robust dataset is constructed with a total of 5233 preprocessed images.Starting from the task’s dual requirements for speed and accuracy,we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety.Experimental results show that compared with three typical deeplearning-based detection models,our method achieves a relatively optimal balance while ensuring high detection accuracy(84.14%),with faster processing time(42.02 ms)after the average analysis of 17 classes of PPE donning situation.Overall,this research focuses on the automatic detection of worker safety protection for the first time in healthcare,which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.