人工智能与机器人研究最新文献_第5页

Construction of a Sample Set of Earthquake-Damaged Buildings Based on Post-Disaster UAV Remote Sensing Images 基于灾后无人机遥感图像的地震受损建筑样本集构建

人工智能与机器人研究 Pub Date : 2022-01-01 DOI: 10.12677/airr.2022.113024

喆董

引用次数: 0

Design of Integrated Logistics and Distribution Robot for Extermination and Transportation 灭杀运输一体化物流配送机器人的设计

人工智能与机器人研究 Pub Date : 2022-01-01 DOI: 10.12677/airr.2022.113027

启巡肖

引用次数: 0

Research on Vehicle Detection in UAV Remote Sensing Image Based on Improved YOLOv5 基于改进YOLOv5的无人机遥感图像车辆检测研究

人工智能与机器人研究 Pub Date : 2022-01-01 DOI: 10.12677/airr.2022.114034

利川耿

引用次数: 0

Lung X-Ray Multi-Label Classification Method Which Is Based on Densely Connected Neural Network 基于密集连接神经网络的肺x射线多标签分类方法

人工智能与机器人研究 Pub Date : 2022-01-01 DOI: 10.12677/airr.2022.112015

财陈

引用次数: 0

Intelligent Logistics Handling Robot Based on STM32 基于STM32的智能物流搬运机器人

人工智能与机器人研究 Pub Date : 2022-01-01 DOI: 10.12677/airr.2022.112011

红樱梅

引用次数: 0

Research on Wheat Ears Recognition Algorithm Based on YOLOv5s Neural Network 基于YOLOv5s神经网络的麦穗识别算法研究

人工智能与机器人研究 Pub Date : 2022-01-01 DOI: 10.12677/airr.2022.112010

月姜

引用次数: 1

Application of Improved Ant Colony Algorithm Based on Harmony Search in UAV Path Planning 基于和谐搜索的改进蚁群算法在无人机路径规划中的应用

人工智能与机器人研究 Pub Date : 2022-01-01 DOI: 10.12677/airr.2022.114038

宁乔

引用次数: 0

Farmland Pest Detection Based on YOLO-V5l and ResNet50 基于YOLO-V5l和ResNet50的农田有害生物检测

人工智能与机器人研究 Pub Date : 2022-01-01 DOI: 10.12677/airr.2022.113025

春源柳

{"title":"Farmland Pest Detection Based on YOLO-V5l and ResNet50","authors":"春源柳","doi":"10.12677/airr.2022.113025","DOIUrl":"https://doi.org/10.12677/airr.2022.113025","url":null,"abstract":"At present, China’s farmland is increasingly affected by insect pests. Insect situation analysis can formulate different plans to control farmland pests according to the insect situation in different regions. Traditional pest situation analysis relies on manual collection and statistics, which is time-consuming and labor-consuming. With the development of deep learning technology in the field of computer vision, this paper proposes to build a farmland pest detection model by combin-ing YOLO-V5l target detection and ResNet50 neural network. Insects have the characteristics of diverse body shapes, missing scales and falling limbs in the image data, which have a great impact on the target detection and classification. Therefore, this paper roughly classifies 28 pests into seven A~G species according to their body shapes and colors, uses YOLO-V5l model to detect and count them, and then substitutes the detection results into ResNet50 recognition model to deter-mine their species. This method greatly reduces the false detection rate of farmland pest detection. Moreover, this paper proposes a predictive enhancement algorithm. After the pest images to be detected are enhanced, they are brought into the recognition model respectively, and the recognition results are weighted average to get the final results. mAP.5:.95 of single YOLO-V5l model was 71.4%, the average accuracy rate was 80.91%, and the missed detection rate was 5.39%. The average accuracy of the pest detection model proposed in this paper is 89.56%, which improves the recognition accuracy of farmland pests. The model will improve the shortcomings of original artificial statistics and promote the development of Intelligent Agriculture in China.","PeriodicalId":68167,"journal":{"name":"人工智能与机器人研究","volume":"215 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79587950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Improved BP Neural Network Character Recognition Algorithm Based on PSO 基于粒子群算法的改进BP神经网络字符识别算法研究

人工智能与机器人研究 Pub Date : 2022-01-01 DOI: 10.12677/airr.2022.113026

成郭

引用次数: 0

Application of Artificial Intelligence Semantic Segmentation Technology in Piano Education 人工智能语义分割技术在钢琴教学中的应用

人工智能与机器人研究 Pub Date : 2022-01-01 DOI: 10.12677/airr.2022.114037

丽敏胡

引用次数: 0