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Automatic rebar counting in the bundled steel bar image require urgent solution in the steel and construction application scenarios. Traditional image processing methods perform badly because the rebar end faces are in irregular shape and highly dense. Focusing on the characteristics of dense small objects in the bundled rebar images, this paper proposes YOLACT_REBAR instance segmentation network. We make improvements from two aspects: network structure and inference process. For network structure improvement, in view of the characteristics of the steel bar end face being a small target, we optimize the original model by connecting the second layer of its backbone network to FPN (Feature Pyramid Network) to obtain larger feature maps, thereby enhancing the model's accuracy in dense small target segmentation. For the inference process, in view of the dense characteristics of steel bar end faces, traditional Non-Maximum value Suppression is used to replace the fast Non-Maximum value Suppression, and the IOU (Intersection-Over-Union) deduplication strategy is used to remove redundant bounding boxes, thereby reducing the network's false detection. Experimental results on self-constructed dataset show that compared with the original YOLACT model and other object detection models, our proposed model demonstrate improved Precision, Recall and mIOU performance. The proposed YOLACT_REBAR model can make a precise segmentation of each rebar end face, which will facilitate subsequent applications such as rebar counting and automatic plate welding.
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