Autonomous construction robots intend to accomplish
indoor and outdoor construction and decoration operations
automatically and efficiently. Among these operations is the
wall putty scraping process, which is very time-consuming and
tedious when accomplished by humans. Through the putty
scraping operation, the putty bulges spread anywhere inside
the working frame. To assist autonomous construction robots
in finalizing the putty scraping operation successfully, the robots
must automatically identify the putty terminal points location.
We propose an end-to-end, trainable hierarchical deep learning
model based on multi-scale attention feature fusion to localize the
putty terminal points precisely. Our model combines the benefits
of visual attention and the inception module to boost the model’s
feature extraction capabilities. Experimental results showed the
effectiveness and performance boost of our proposed model when
applied to different ResNet architectures. |
