QCNet: query context network for salient object detection of automatic surface inspection

Abstract

AbstractBuilding upon fully convolutional networks (FCNs), deep learning-based salient object detection (SOD) methods achieve gratifying performance in many vision tasks, including surface defect detection. However, most existing FCN-based methods still suffer from the coarse object edge predictions. The state-of-the-art methods employ intricate feature aggregation techniques to refine boundaries, but they are often too computational cost to deploy in the real application. This paper proposes a semantics guided detection paradigm for salient object detection. Guided atrous pyramid module is first applied on the top feature to segment complete salient semantics. Query context modules are further used to build relation maps between saliency and structural information from the top-down pathway. These two modules allow the semantic features to flow throughout the decoder phase, yielding detail enriched saliency predictions. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods on surface defect detection and SOD benchmarks. In addition, this method can detect at 27 FPS in a fully convolutional fashion without any post-processing, which has the potential for real-time detection.