Detection Method for Sweet Cherry Fruits Based on YOLOv4 in the Natural Environment

[Objectives]To explore a rapid detection method of sweet cherry fruits in natural environment.[Methods]The cutting-edge YOLOv4 deep learning model was used.The YOLOv4 detection model was built on the CSP Darknet5 framework.A mosaic data enhancement method was used to expand the image dataset,and the model was processed to facilitate the detection of three different occlusion situations:no occlusion,branch and leaf occlusion,and fruit overlap occlusion,and the detection of sweet cherry fruits with different fruit numbers.[Results]In the three occlusion cases,the mean average precision(mAP)of the YOLOv4 algorithm was 95.40%,95.23%,and 92.73%,respectively.Different numbers of sweet cherry fruits were detected and identified,and the average value of mAP was 81.00%.To verify the detection performance of the YOLOv4 model for sweet cherry fruits,the model was compared with YOLOv3,SSD,and Faster-RCNN.The mAP of the YOLOv4 model was 90.89%and the detection speed was 22.86 f/s.The mAP was 0.66%,1.97%,and 12.46%higher than those of the other three algorithms.The detection speed met the actual production needs.[Conclusions]The YOLOv4 model is valuable for picking and identifying sweet cherry fruits.

Research on the Application of Helmet Detection Based on YOLOv4

Helmets are one of the important measures to ensure the safety of construction workers. Because the harm caused by not wearing safety helmets as required is great, the wearing of safety helmets has also attracted more and more people’s attention. At present, the main method of helmet detection is the YOLO series of algorithms. They often only focus on detection accuracy, ignoring the actual situation during deployment, that is, a balance between accuracy and speed is required. Therefore, this paper proposes a helmet detection application based on YOLOv4 algorithm, and combined with the MobileNet network, it has achieved good results in terms of detection accuracy and speed. Through transfer learning and tuning parameters, the mAP and FPS values detected in this paper on the public safety helmet datasets are 94.47% and 27.36%, which exceed the research work of some similar papers. This paper also combines YOLOv4 and MobileNetv3 networks to propose a mobileNet-based YOLOv4 helmet detection application. Its mAP and FPS values are 91.47% and 42.58%, respectively, which meet the accuracy and real-time requirements of current hardware deployment.

An Improved Algorithm for the Detection of Fastening Targets Based on Machine Vision

Object detection plays an important role in the sorting process of mechanical fasteners.Although object detection has been studied for many years,it has always been an industrial problem.Edge-based model matching is only suitable for a small range of illumination changes,and the matching accuracy is low.The optical flow method and the difference method are sensitive to noise and light,and camshift tracking is less effective in complex backgrounds.In this paper,an improved target detection method based on YOLOv3-tiny is proposed.The redundant regression box generated by the prediction network is filtered by soft nonmaximum suppression(NMS)instead of the hard decision NMS algorithm.This not only increases the size of the network structure by 52×52 and improves the detection accuracy of small targets but also uses the basic structure block MobileNetv2 in the feature extraction network,which enhances the feature extraction ability with the increased network layer and improves network performance.The experimental results show that the improved YOLOv3-tiny target detection algorithm improves the detection ability of bolts,nuts,screws and gaskets.The accuracy of a single type has been improved,which shows that the network greatly enhances the ability to learn objects with slightly complex features.The detection result of single shape features is slightly improved,which is higher than the recognition accuracy of other types.The average accuracy is increased from 0.813 to 0.839,an increase of two percentage points.The recall rate is increased from 0.804 to 0.821.

Automatic tunnel lining crack detection via deep learning with generative adversarial network-based data augmentation

Aiming at solving the challenges of insufficient data samples and low detection efficiency in tunnel lining crack detection methods based on deep learning,a novel detection approach for tunnel lining crack was proposed,which is based on pruned You Look Only Once v4(YOLOv4)and Wasserstein Generative Adversarial Network enhanced by Residual Block and Efficient Channel Attention Module(WGAN-RE).In this study,a data augmentation method named WGAN-RE was proposed,which can achieve the automatic generation of crack images to enrich data set.Furthermore,YOLOv4 was selected as the basic model for training,and a pruning algo-rithm was introduced to lighten the model size,thereby effectively improving the detection speed.Average Precision(AP),F1 Score(F1),model size,and Frames Per Second(FPS)were selected as evaluation indexes of the model performance.Results indicate that the storage space of the pruned YOLOv4 model is only 49.16 MB,which is 80%compressed compared with the model before pruning.In addition,the FPS of the model reaches 40.58f/s,which provides a basis for the real-time detection of tunnel lining cracks.Findings also demon-strate that the F1 score and AP of the pruned YOLOv4 are only 0.77%and 0.50%lower than that before pruning,respectively.Besides,the pruned YOLOv4 is superior in both model accuracy and efficiency compared with YOLOv3,SSD,and Faster RCNN,which indi-cated that the pruned YOLOv4 model can realize the accurate,fast and intelligent detection of tunnel lining cracks in practical tunnel engineering.

Precise and efficient Chinese license plate recognition in the real monitoring scene of intelligent transportation system

In this paper, the performance of you only look once(YOLO) series detectors on Chinese license plate recognition(LPR) in the real intelligent transportation system(ITS) monitoring scene is investigated. Specially, a precise and efficient automatic license plate recognition(ALPR) system based on the YOLOv4 detector is proposed. The proposed ALPR system contains three stages including vehicle detection, license plate detection(LPD) and LPR. In vehicle detection stage, YOLOv4 detector is directly applied. In LPD stage, YOLOv4-tiny detector is exploited. In the last stage, the YOLOv4-tiny detector with attention mechanism for LPR is proposed to use. In addition, a large Chinese license plate dataset containing 10 500 images collected from all 31 provinces in the Chinese mainland is created. This Chinese license plate dataset is named Hefei University of Technology license plate version 1(HFUT-LP v1). Particularly, HFUT-LP v1 dataset is collected in the real ITS monitoring scene. In order to compare the performance of different object detection algorithms for ALPR, a variety of object detection algorithms are used to make a comprehensive performance evaluation. Experimental results show that the proposed ALPR system achieves very high accuracy and has very fast processing speed, which is suitable for real-time LPR.