基于YOLO模型的小麦外观分类算法研究

小麦种植广泛且营养丰富,其品质问题需要重点关注。小麦品质的衡量指标主要是不完善粒占比。为此,需要对小麦颗粒进行分类识别。提出了1种基于你只看一次(YOLO)模型的小麦外观自动分类算法,创新性地将YOLO模型应用于小麦外观分类场景。对采集得到的小麦样本图像切割、筛选、扩充和标记,构建了完善粒与不完善粒图像库。对YOLO网络进行了训练,利用训练后的模型对麦粒图像进行了测试,实现了完善粒、不完善粒分别为91.7%、87.1%的分类准确率。这种自动分拣麦粒的检验方法避免了人工视觉疲劳后的误判,而且检测效率显著提高,为小麦外观分类研究提供了新的思路。

基于知识图谱的变压器匝间短路故障辨识研究

变压器出现的故障数据间的关联性没有被较好地利用,会直接影响变压器匝间短路故障的辨识准确性。为此,提出基于知识图谱的变压器匝间短路故障辨识研究。基于柔性策略采集变压器数据,根据实际运行情况及数据采集目标需求,调整数据采集量及时间间隔。根据采集到的变压器数据,采用本体构建、实体抽取、关系抽取和图谱构建的步骤构建知识图谱。将提取的知识图谱故障样本特征,输入到你只看一次(YOLO)v4检测模型中。通过YOLOv4检测模型与知识图谱结合的检测方法,完成变压器匝间短路故障的自动辨识。试验结果表明:变压器匝间短路故障自动辨识的准确率、召回率和F值均较高,因而辨别及时性高、自动辨别效果好。该研究解决了传统方法中存在的问题,具有重要的现实意义。

A Fast Tongue Detection and Location Algorithm in Natural Environment

The collection and extraction of tongue images has always been an important part of intelligent tongue diagnosis.At present,the collection of tongue images generally needs to be completed in a sealed,stable light environment,which is not conducive to the promotion of extensive tongue image and intelligent tongue diagnosis.In response to the problem,a newalgorithm named GCYTD(GELU-CA-YOLO Tongue Detection)is proposed to quickly detect and locate the tongue in a natural environment,which can greatly reduce the restriction of the tongue image collection environment.The algorithm is based on the YOLO(You Only Look Once)V4-tiny network model to detect the tongue.Firstly,the GELU(Gaussian Error Liner Units)activation function is integrated into the model to improve the training speed and reduce the number of model parameters;then,the CA(Coordinate Attention)mechanism is integrated into the model to enhance the detection precision and improve the failure tolerance of the model.Compared with the other classical algorithms,Experimental results show thatGCYTD algorithm has a better performance on the tongue images of all types in terms of training speed,tongue detection speed and detection precision,etc.The lighter model can contribute on deploying the tongue detection model on small mobile terminals.

基于改进ShuffleNetV2的轻量级花色布匹瑕疵检测

布匹瑕疵检测是纺织业质量管理的重要环节.在嵌入式设备上实现准确、快速的布匹瑕疵检测能有效降低成本,因而价值巨大.考虑到实际生产中花色布匹瑕疵具有背景复杂、数量差异大、极端长宽比和小瑕疵占比高等结构特性,提出一种基于轻量级模型的花色布匹瑕疵检测方法并将其部署在嵌入式设备Raspberry Pi 4B上.首先在一阶段目标检测网络YOLO的基础上用轻量级特征提取网络ShuffleNetV2提取花色布匹瑕疵的特征,以减少网络结构复杂度及参数量,提升检测速度;其次是检测头的解耦合,将分类与定位任务分离,以提升模型收敛速度;此外引入CIoU作为瑕疵位置回归损失函数,提高瑕疵定位准确性.实验结果表明,本文算法在Raspberry Pi 4B上可达8.6 FPS的检测速度,可满足纺织工业应用需求.

一种基于视觉识别的乒乓球捡球机设计与开发

针对目前乒乓球捡球机捡球机构不完善、乒乓球识别算法适应性差的问题,提出一种基于视觉识别的智能乒乓球捡球机.采用树莓派4B开发板作为控制单元,利用轻量化的you only look once(YOLO)v5s算法,对乒乓球进行识别;通过扇叶式集球机构,将乒乓球卷入收纳篮.实验结果表明:在乒乓球数小于150个的情况下,该捡球机的识别精确率与查全率均可达到95%以上,漏检率控制在7%以下.同时,集球机构结构简单、可靠、效率高,整体设计方案具有较好的实际应用价值.

Enhanced Deep Learning for Detecting Suspicious Fall Event in Video Data

Suspicious fall events are particularly significant hazards for the safety of patients and elders.Recently,suspicious fall event detection has become a robust research case in real-time monitoring.This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving back-grounds in an indoor environment;it is further proposed to use a deep learning method known as Long Short Term Memory(LSTM)by introducing visual atten-tion-guided mechanism along with a bi-directional LSTM model.This method contributes essential information on the temporal and spatial locations of‘suspi-cious fall’events in learning the video frame in both forward and backward direc-tions.The effective“You only look once V4”(YOLO V4)–a real-time people detection system illustrates the detection of people in videos,followed by a track-ing module to get their trajectories.Convolutional Neural Network(CNN)fea-tures are extracted for each person tracked through bounding boxes.Subsequently,a visual attention-guided Bi-directional LSTM model is proposed for the final suspicious fall event detection.The proposed method is demonstrated using two different datasets to illustrate the efficiency.The proposed method is evaluated by comparing it with other state-of-the-art methods,showing that it achieves 96.9%accuracy,good performance,and robustness.Hence,it is accep-table to monitor and detect suspicious fall events.

轻量化的PCB表面缺陷检测算法

针对印刷电路板(PCB)表面缺陷检测存在的速度低和准确率不高等问题,提出了一种基于改进YOLOv4-tiny模型的PCB表面缺陷检测算法。首先,采用了优化后的聚类方法对缺陷数据集进行聚类,以解决初始先验框不适合PCB表面缺陷检测的问题;其次,为了解决主干网络在下采样时可能丢失小尺度目标信息的问题,引入了切片操作;接着,在特征融合网络中,引入了软池化卷积结构,以提高模型感受野,增强对小目标特征的表达能力;最后,通过引入改进后的交叉熵损失函数优化了损失函数。在北京大学开源的印刷电路板缺陷数据集上验证了所提算法的效果,结果表明,相较于其他经典算法,所提算法在检测速度、精度和模型参数量等指标上都有较大的提升。

Non-destructive thermal imaging for object detection via advanced deep learning for robotic inspection and harvesting of chili peppers

Deep Learning has been utilized in computer vision for object detection for almost a decade.Real-time object detection for robotic inspection and harvesting has gained interest during this time as a possible technique for highqualitymachine assistance during agriculture applications.We utilize RGB and thermal images of chili peppers in an environment of various amounts of debris,pepper overlapping,and ambient lighting,train this dataset,and compare object detection methods.Results are presented from the real-time and less than real-time object detection models.Two advanced deep learning algorithms,Mask-Regional Convolutional Neural Networks(Mask-RCNN)and You Only Look Once version 3(YOLOv3)are compared in terms of object detection accuracy and computational costs.When utilizing the YOLOv3 architecture,an overall training mean average precision(mAP)value of 1.0 is achieved.Most testing images from this model score within a range from 97 to 100%confidence levels in natural environment.It is shown that the YOLOv3 algorithm has superior capabilities to the Mask-RCNNwith over 10 times the computational speed on the chili dataset.However,some of the RGB test images resulted in lowclassification scoreswhen heavy debris is present in the image.A significant improvement in the real-time classification scores was observed when the thermal images were used,especially with heavy debris present.We found and report improved prediction scores with a thermal imagery dataset where YOLOv3 struggled on the RGB images.It was shown that mapping temperature differences between the pepper and plant/debris can provide significant features for object detection in real-time and can help improve accuracy of predictionswith heavy debris,variant ambient lighting,and overlapping of peppers.In addition,successful thermal imaging for real-time robotic harvesting could allow the harvesting period to become more efficient and open up harvesting opportunity in low light situations.