Shadow detection is crucial for accurate scene understanding in computer vision, yet it is challenged by the diverse appearances of shadows caused by variations in illumination, object geometry, and scene context. Deep learning models often struggle to generalize to real-world images due to the limited size and diversity of training datasets. To address this, we introduce TICA, a novel framework that leverages light-intensity information during test-time adaptation to enhance shadow detection accuracy. TICA exploits the inherent inconsistencies in light intensity across shadow regions to guide the model toward a more consistent prediction. A basic encoder-decoder model is initially trained on a labeled dataset for shadow detection. Then, during the testing phase, the network is adjusted for each test sample by enforcing consistent intensity predictions between two augmented input image versions. This consistency training specifically targets both foreground and background intersection regions to identify shadow regions within images accurately for robust adaptation. Extensive evaluations on the ISTD and SBU shadow detection datasets reveal that TICA significantly demonstrates that TICA outperforms existing state-of-the-art methods, achieving superior results in balanced error rate (BER).

翻译：阴影检测对于计算机视觉中准确理解场景至关重要，然而，由于光照变化、物体几何形状和场景背景的多样性导致的阴影外观各异，使其面临挑战。由于训练数据集的规模和多样性有限，深度学习模型往往难以泛化到真实世界图像。为解决此问题，我们引入了TICA，一种新颖的框架，在测试时自适应过程中利用光强信息来提升阴影检测精度。TICA利用阴影区域间光强的固有不一致性来引导模型做出更一致的预测。首先，一个基础的编码器-解码器模型在有标签的阴影检测数据集上进行训练。随后，在测试阶段，通过强制两个增强输入图像版本之间的强度预测保持一致，网络针对每个测试样本进行调整。这种一致性训练特别针对前景和背景交叉区域，以准确识别图像中的阴影区域，实现稳健的自适应。在ISTD和SBU阴影检测数据集上的广泛评估表明，TICA显著优于现有最先进方法，在平衡错误率（BER）上取得了更优的结果。