R. Sinnott, Donghan Yang, Xueyang Ding, Zhenyuan Ye
{"title":"通过深度学习识别毒蜘蛛","authors":"R. Sinnott, Donghan Yang, Xueyang Ding, Zhenyuan Ye","doi":"10.1145/3373017.3373031","DOIUrl":null,"url":null,"abstract":"Deep learning and neural networks have recently gained considerable attention and are now one of the most popular topics in modern computer science. One of the most promising applications of deep learning is in the field of computer vision and especially in the application of convolutional neural networks (CNNs) for object detection and classification of images. In this paper, we explore various CNN models to identify and classify common species of spiders found in Australia with specific focus on poisonous spiders. We compare the accuracy and performance of the deep learning models on a range of diverse spider species. We also develop an iOS application as the front-end user application.","PeriodicalId":297760,"journal":{"name":"Proceedings of the Australasian Computer Science Week Multiconference","volume":"154 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Poisonous Spider Recognition through Deep Learning\",\"authors\":\"R. Sinnott, Donghan Yang, Xueyang Ding, Zhenyuan Ye\",\"doi\":\"10.1145/3373017.3373031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep learning and neural networks have recently gained considerable attention and are now one of the most popular topics in modern computer science. One of the most promising applications of deep learning is in the field of computer vision and especially in the application of convolutional neural networks (CNNs) for object detection and classification of images. In this paper, we explore various CNN models to identify and classify common species of spiders found in Australia with specific focus on poisonous spiders. We compare the accuracy and performance of the deep learning models on a range of diverse spider species. We also develop an iOS application as the front-end user application.\",\"PeriodicalId\":297760,\"journal\":{\"name\":\"Proceedings of the Australasian Computer Science Week Multiconference\",\"volume\":\"154 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Australasian Computer Science Week Multiconference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3373017.3373031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Australasian Computer Science Week Multiconference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3373017.3373031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Poisonous Spider Recognition through Deep Learning
Deep learning and neural networks have recently gained considerable attention and are now one of the most popular topics in modern computer science. One of the most promising applications of deep learning is in the field of computer vision and especially in the application of convolutional neural networks (CNNs) for object detection and classification of images. In this paper, we explore various CNN models to identify and classify common species of spiders found in Australia with specific focus on poisonous spiders. We compare the accuracy and performance of the deep learning models on a range of diverse spider species. We also develop an iOS application as the front-end user application.