The stability of mine dumps is contingent upon the precise arrangement of
spoil piles, taking into account their geological and geotechnical attributes.
Yet, on-site characterisation of individual piles poses a formidable challenge.
The utilisation of image-based techniques for spoil pile characterisation,
employing remotely acquired data through unmanned aerial systems, is a
promising complementary solution. Image processing, such as object-based
classification and feature extraction, are dependent upon effective
segmentation. This study refines and juxtaposes various segmentation
approaches, specifically colour-based and morphology-based techniques. The
objective is to enhance and evaluate avenues for object-based analysis for
spoil characterisation within the context of mining environments. Furthermore,
a comparative analysis is conducted between conventional segmentation
approaches and those rooted in deep learning methodologies. Among the diverse
segmentation approaches evaluated, the morphology-based deep learning
segmentation approach, Segment Anything Model (SAM), exhibited superior
performance in comparison to other approaches. This outcome underscores the
efficacy of incorporating advanced morphological and deep learning techniques
for accurate and efficient spoil pile characterisation. The findings of this
study contribute valuable insights to the optimisation of segmentation
strategies, thereby advancing the application of image-based techniques for the
characterisation of spoil piles in mining environments.

基于图像的矿山废弃物堆积物表征的优化，利用远程获取的数据和深度学习方法，通过各种分割方法的比较分析，展示了基于形态学和深度学习方法的高准确性和高效率，推动了矿山环境中基于图像的堆积物表征技术的应用。

基于无人机图像的废矽片描绘的传统方法与深度学习方法的比较评估

Comparative Evaluation of Traditional and Deep Learning-Based  Segmentation Methods for Spoil Pile Delineation Using UAV Images

A Convolutional Neural Network architecture was used to classify various
isotopes of time-sequenced gamma-ray spectra, a typical output of a radiation
detection system of a type commonly fielded for security or environmental
measurement purposes. A two-dimensional surface (waterfall plot) in time-energy
space is interpreted as a monochromatic image and standard image-based CNN
techniques are applied. This allows for the time-sequenced aspects of features
in the data to be discovered by the network, as opposed to standard algorithms
which arbitrarily time bin the data to satisfy the intuition of a human
spectroscopist. The CNN architecture and results are presented along with a
comparison to conventional techniques. The results of this novel application of
image processing techniques to radiation data will be presented along with a
comparison to more conventional adaptive methods.

利用卷积神经网络 (CNN) 架构对时间序列伽马射线谱进行分类，以实现辐射检测系统对于安全或环境测量目的。瀑布图被解释为一幅单色图像，并采用标准基于图像的 CNN 技术，以发现数据中特征的时序方面。同时，将 CNN 架构和结果与传统技术进行比较，这样的图像处理技术的结果将被介绍，并与更传统的应适应方法进行比较。