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In order to solve the problem of automatic target-scoring systems, which is finding target sheet regions quickly and accurately from image, we propose a fast detection algorithm of target sheets based on Improved Fast Minimum Barrier Distance Saliency Detection and Multi-Feature Fusion Matching by analyzing the color character and the shape character of target sheets. We introduce local regional contrast prior knowledge and shape prior knowledge that are the compensation extraction criteria of salient regions to the origin Fast MBD Saliency Detection. Meanwhile, to determine whether the extracted region contains a target sheet, we introduce Multi-feature Fusion Matching. Firstly, we quantify image boundary connectivity prior knowledge, local regional contrast prior knowledge and shape prior knowledge respectively to calculate distance map, contrast map and shape map. Then we incorporate the three maps to segment salient regions. When we get salient regions, we extract their multi-feature to measure similarity with saved model feature by L1 norm. Finally, we regard the salient region whose similarity measuring value is less than threshold as target sheet. Experimental results on the dataset that has 400 images containing target sheets show that the algorithm we proposed is effective. Meanwhile, it is enough to prove real-time that the speed of the algorithm we proposed will achieve 30FPS at Hua Wei HiSilicon platform.