STDC-MA方法在小物体上更平滑、更准确。在第一行中,STDC-MA获得了比STDC-Seg网络更准确的路灯Mask。在第二排和第三排,STDC-Seg错误地预测了栏杆。在第4行和第5行,STDC-MA在预测行人方面表现出更平滑的结果,很接近于GT,并且优于STDC-Seg网络。 4参考 [1].STDC-MA NETWORK FOR SEMANTIC SEGMENTATION 5...
Fast semantic segmentation for remote sensing images with an improved Short-Term Dense-Connection (STDC) networkMengjia LiuPeng LiuLingjun ZhaoYan MaLajiao ChenMengzhen Xu
Real time segmentation method for underground track area based on improved STDC MA Tian 1, LI Fanhui 1, YANG Jiayi 1, ZHANG Jiehui 1, DING Xuhan 2 (1. College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China ；2. College ...
CGNet: A Light-weight Context Guided Network for Semantic Segmentation ↩ ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time ↩ DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation ↩ Deep Dual-resolution Networks for Real-time and Accurate...
2104.13188:Rethinking BiSeNet For Real-time Semantic Segmentation 创新点 Short-Term Dense Concatenate(STDC): 在BiSeNet(context path + spatial path)的基础上,对有效但极耗时的 spatial path 进行了 去冗余 。 逐步降低特征图的维度,并利用它们的聚合来表示图像,以此形成 STDC 网络的基本模块。
We create a new dataset for UAV aerial semantic segmentation and achieve recognition of emergency landing zones, protected targets, and buildings during high-altitude UAV flight missions. A lightweight semantic segmentation network named STDC-CT i is proposed for UAV emergency landing zones recognition...
We present STDC-Seg, an mannully designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods. Highlights: Short-Term Dense Concatenation Net: A task-specific network for dense prediction task. Detail Guidance: encode spatial information...
A total of 600 UAV aerial images were densely annotated with 12 semantic categories. Given the complex backgrounds, diverse categories, and small UAV aerial image targets, we propose the STDC-CT real-time semantic segmentation network for UAV recognition of emergency landing zones. ...
CGNet: A Light-weight Context Guided Network for Semantic Segmentation[codes] Abstract: The demand of applying semantic segmentation model on mobile devices has been increasing rapidly. Current state-of-the-art networks have enormous amount of parameters hence unsuitable for mobile devices, while other...
CGNet: A Light-weight Context Guided Network for Semantic Segmentation[codes] Abstract: The demand of applying semantic segmentation model on mobile devices has been increasing rapidly. Current state-of-the-art networks have enormous amount of parameters hence unsuitable for mobile devices, while other...