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Affordance detection is of great importance in robot operational tasks, due to its capability of helping robots effectively interact with objects. Many affordance detectors have been proposed, primarily based on two-stage object detection, significantly suffering from the slow detection speed. Hence, recent years have saw the popularity of one-stage affordance detectors based on encoder-decoder structures that adopt dilated convolutions to extract high-resolution feature maps. However, dilated convolutions on high resolution features tend to be computation and memory-intensive, greatly limiting the practicality of one-stage detectors. To address the issue, this paper proposes a novel convolution neural network (CNN) based encoder-decoder architecture, without the need of adopting dilated convolution. A repeated multi-scale feature-map-fusion network is introduced to produce high-resolution features, effectively …