Object Detection in 3D Coral Ecosystem Maps from Multiple Image Sequences
Object Detection in 3D Coral Ecosystem Maps from Multiple Image Sequences
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DOI：
10.1109/icpr56361.2022.9956032
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
S. Bhandarkar;Sushanth Kathirvelu;B. Hopkinson
中科院分区：
文献类型：
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作者：
S. Bhandarkar;Sushanth Kathirvelu;B. Hopkinson
珊瑚礁是生物多样性和结构复杂的生态系统，受到自然和人为压力的严重影响。因此，有必要对珊瑚礁进行快速和准确的生态评估，但目前的方法需要耗费时间的人工数据采集和分析。我们提出了一个计划，以确定和本地化的珊瑚礁生态系统中的个体作为不同的三维对象，并评估其性能。给定RGB图像中的2D区域建议，我们的方法针对每个2D区域建议，基于现有的注释3D礁石重建和与RGB图像相关联的内在和外在相机参数生成3D区域建议。注释的3D珊瑚礁重建使用商业运动结构（SfM）软件和先前设计的用于3D语义分割的多视图卷积神经网络（CNN）生成。由于个体珊瑚礁实体经常在多个图像中被查看，因此3D边界框合并策略与重叠准则相结合用于将多个3D区域提议联合收割机组合成单个3D对象预测以用于分类和定位的目的。实验结果表明，该方案对珊瑚礁调查图像的有效性，并与FrustumPointNet架构进行了比较。
Coral reefs are biologically diverse and structurally complex ecosystems that have been severely affected by natural and anthropogenic stressors. Consequently, there is a need for rapid and accurate ecological assessment of coral reefs, but current approaches entail time-consuming manual data acquisition and analysis. We propose a scheme to identify and localize individual entities within the coral reef ecosystem as distinct 3D objects and assess its performance. Given 2D region proposals in an RGB image, our method generates, for each 2D region proposal, a 3D region proposal based on an existing annotated 3D reef reconstruction and the intrinsic and extrinsic camera parameters associated with the RGB image. The annotated 3D reef reconstruction is generated using a commercial Structure-from-Motion (SfM) software and a previously designed multiview convolutional neural network (CNN) for 3D semantic segmentation. As individual coral reef entities are often viewed in multiple images, a 3D bounding-box merging strategy coupled with an overlap criterion are used to combine multiple 3D region proposals into a single 3D object prediction for the purpose of classification and localization. Experimental results and comparison with the Frustum PointNet architecture show the efficacy of the proposed scheme on coral reef survey images.