## 1 Introduction
### Applications of Computer Vision Technology  
Bulk material piles in aerial imagery were suggested to be segmented using computer vision (CV) in [1], thereby automating inventory control in industries such as mining and construction. Reliance on assumptions about heap shapes or material types is avoided by the method, with color, texture, and surface topography being utilized as input factors. Manmade heaps can be distinguished from natural features, nearby heaps of the same material can be identified, and irregularly shaped heaps can be divided. The method's potential is demonstrated by F1 scores of 0.90 for pixelwise segmentation and 0.70 for object recognition. The potential utilization of FinTech solutions driven by deep learning for sustainable mineral extraction and use was investigated in [2]. A quantized variation in the efficient deep learning model was proposed for mineral identification, resulting in reduced model size and complexity while performance was maintained. An 8-bit symmetric quantization technique was applied to a specific mineral dataset, yielding improved results. A precision of 0.97, a recall of 0.89, and an accuracy of 99.5% were achieved by the quantized model, surpassing the performance of the floating-point model. By forecasting mineral concentrate grades using image processing and deep learning techniques, Refs. [3; 4] address the challenge of controlling the froth flotation process in the mining industry. For Zn, Pb, Fe, and Cu concentration grades in real-world industrial evaluations, image processing was utilized to extract important froth properties, including texture, bubble size, velocity, and color distribution, and obtained high accuracy (up to 0.94) and presented precision errors below 4.53%. These results highlight the potential of the method as an AI-driven online analyzer for improving the performance and real-time control of complex polymetallic flotation circuits. The importance of appropriately monitoring spoil pile heights in the dragline overburden (OB) removal operation was addressed in [5]. This is a practice required for in-pit space optimization and, concurrently, safety and productivity in mining operations. This operation entails real-time monitoring of dump pile heights to confirm that they are in compliance with dragline balancing diagrams for the offering of stable dump profiles. The proposed method estimates dump pile heights in real-time with an 84.6% F1-confidence score and a 99.49% mean average precision (mAP) using the YOLO technique. Using 2D computer vision on simulated video data, the method provides a quick, cheap, real-time dump profile recognition and pile height estimate system. This invention guarantees that stable and safe dump heights with the given design criteria are maintained, therefore increasing the production of dragline mining. Using 2042 training photos and 284 new images across 68 types, Ref. [6] used a tech based on how computers see to sort gems by type on their own. They checked these photos in just 0.0165 s using a top model that mixed Random Forest with RGB color data and local binary patterns and obtained a 69.4% hit rate. This result was the same as that of human gem experts, who took 42 to 175 min to sort the same photos but obtained scores ranging from 42.6% to 66.9%. Even though the human experts were slower, the computer techn was a bit more on target, showing how useful this tool could be in studying gems and similar fields. Examining how an anti-collision system with computer sight sees persons traveling in deep mines, major safety concerns like poor sight and concealed places that might cause major incidents and deaths are addressed in [7]. Following PRISMA guidelines, a complete review sorts past systems based on the kind of sensor (i.e., cameras or lidar) and evaluates their performance in deep, dark environments.