Visual explanation of "black-box" models has enabled researchers and experts in artificial intelligence (AI) to exploit the localization abilities of such methods to a much greater extent. Despite most of the developed visual explanation methods applied to single object classification problems, they are not well-explored in the detection task, where the challenges may go beyond simple coarse area-based discrimination. This is of particular importance when a detector should face several objects with different scales from various viewpoints or if the objects of interest are absent. In this paper, we propose CrownCAM to generate reliable visual explanations for the challenging and dynamic problem of tree crown detection in aerial images. It efficiently provides fine-grain localization of tree crowns and non-contextual background suppression for scenarios with highly dense forest trees in the presence of potential distractors or scenes without tree crowns. Additionally, two Intersection over Union (IoU)-based metrics are introduced that can effectively quantify both the accuracy and inaccuracy of generated visual explanations with respect to regions with or without tree crowns in the image. Empirical evaluations demonstrate that the proposed Crown-CAM outperforms the Score-CAM, Augmented ScoreCAM, and Eigen-CAM methods by an average IoU margin of 8.7, 5.3, and 21.7 (and 3.3, 9.8, and 16.5) respectively in improving the accuracy (and decreasing inaccuracy) of visual explanations on the challenging NEON tree crown dataset.
翻译:对“黑盒子”模型的直观解释使人工智能(AI)研究人员和专家能够更深入地利用这些方法的本地化能力。尽管对单一物体分类问题应用了大多数成熟的直观解释方法,但在探测任务中并没有很好地探索这些方法,其中的挑战可能超越简单粗糙的基于地区的歧视。当探测器从不同角度或没有感兴趣的对象而面对不同尺度的多个对象时,这一点特别重要。在本文件中,我们建议皇家气候研究中心为在空中图像中探测树冠的具有挑战性和动态的问题提供可靠的直观解释。它有效地为树冠的本地化提供了精细刻和不通的背景抑制,在可能发生偏差或没有树冠的场景时,这些方法在探测任务中并没有很好地探索。此外,引入了两个基于联合(IoU)的交叉度指标,可以有效地量化对有或没有树冠的区域产生的直观解释的准确性和不准确性。我们建议皇家气候研究中心对空中图像探测树冠的具有挑战性的问题进行可靠的直观解释。它有效地提供了精细微的树冠和不直径直径直径直径直径直径直径直径直径直径直径直径直径直径分析(E-C,A-C在198、直径C的直径C的直径、直径直径直径C的直径直径直径、直径直径直径、亚、直径直径直径直径直径直径直径直至、直至、直径、直至、直径直径、直至直至直至直径直径直至、直径直径直径直径直径直径直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直至直直至直至直至直至直至直至直至直至直至直至直至直至直至直