{"title":"利用接受域预训练编码器和压缩激励模块进行道路分割","authors":"Anamika Maurya, S. Chand","doi":"10.1109/SPIN52536.2021.9565944","DOIUrl":null,"url":null,"abstract":"Autonomous vehicles will decrease the number of accidents on the road caused by human error. Intelligent vehicles have traditionally advanced in a step-by-step manner. These developments boost the automation scene in vehicles by incorporating systems that facilitate the driver in maintaining a constant speed, adhering to a lane, or transferring control over vehicle and driver. Autonomous vehicles must have a thorough understanding of their surroundings. As a result, object detection and road scene segmentation are critical in navigation for recognizing the drivable and non-drivable areas. Towards the development of the completely automated framework for road scene segmentation, we propose an RFB-SELinkNet that utilizes the SEResNeXt model as a feature extractor and receptive field block (RFB) with squeeze and excitation (SE) module for better feature representations. Our proposed framework outperforms D-LinkNet, Eff-UNet, and other state-of-art models. According to the experiments, the proposed model achieves 0.698 mloU and produces good segmentation outcomes on the validation set of the India Driving Lite (IDD Lite) dataset.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Exploiting Pre-trained Encoder with Receptive Fields and Squeeze-Excitation module for Road Segmentation\",\"authors\":\"Anamika Maurya, S. Chand\",\"doi\":\"10.1109/SPIN52536.2021.9565944\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Autonomous vehicles will decrease the number of accidents on the road caused by human error. Intelligent vehicles have traditionally advanced in a step-by-step manner. These developments boost the automation scene in vehicles by incorporating systems that facilitate the driver in maintaining a constant speed, adhering to a lane, or transferring control over vehicle and driver. Autonomous vehicles must have a thorough understanding of their surroundings. As a result, object detection and road scene segmentation are critical in navigation for recognizing the drivable and non-drivable areas. Towards the development of the completely automated framework for road scene segmentation, we propose an RFB-SELinkNet that utilizes the SEResNeXt model as a feature extractor and receptive field block (RFB) with squeeze and excitation (SE) module for better feature representations. Our proposed framework outperforms D-LinkNet, Eff-UNet, and other state-of-art models. According to the experiments, the proposed model achieves 0.698 mloU and produces good segmentation outcomes on the validation set of the India Driving Lite (IDD Lite) dataset.\",\"PeriodicalId\":343177,\"journal\":{\"name\":\"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPIN52536.2021.9565944\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPIN52536.2021.9565944","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Exploiting Pre-trained Encoder with Receptive Fields and Squeeze-Excitation module for Road Segmentation
Autonomous vehicles will decrease the number of accidents on the road caused by human error. Intelligent vehicles have traditionally advanced in a step-by-step manner. These developments boost the automation scene in vehicles by incorporating systems that facilitate the driver in maintaining a constant speed, adhering to a lane, or transferring control over vehicle and driver. Autonomous vehicles must have a thorough understanding of their surroundings. As a result, object detection and road scene segmentation are critical in navigation for recognizing the drivable and non-drivable areas. Towards the development of the completely automated framework for road scene segmentation, we propose an RFB-SELinkNet that utilizes the SEResNeXt model as a feature extractor and receptive field block (RFB) with squeeze and excitation (SE) module for better feature representations. Our proposed framework outperforms D-LinkNet, Eff-UNet, and other state-of-art models. According to the experiments, the proposed model achieves 0.698 mloU and produces good segmentation outcomes on the validation set of the India Driving Lite (IDD Lite) dataset.