Coal Mining enterprises deploy numerous monitoring devices to ensure safe and efficient production using target detection technologies. However, deploying deep detection models on edge devices poses challenges due to high computational loads, impacting detection speed and accuracy. A mining target detection dataset has been created to address these issues, featuring key targets in coal mining scenes such as miners, safety helmets, and coal gangue. A model is proposed to improve real‐time performance for edge mining detection tasks. Detection performance is enhanced by incorporating a Pixel‐wise Normalization Spatial Attention Module (PN‐SAM) into the MobileNet‐v3 bneck structure and replacing the h‐swish activation function with Mish, providing more prosperous gradient information transfer. The proposed model, YOLO‐v4‐LSAM, shows a 3.2% mAP improvement on the VOC2012 dataset and a 2.4% improvement on the mining target dataset compared to YOLO‐v4‐Tiny, demonstrating its effectiveness in mining environments. These enhancements enable more accurate and efficient detection in resource‐constrained edge environments, contributing to safer and more reliable monitoring in coal mining operations.

Considering the challenges of low multi‐object detection accuracy and difficulty in identifying small targets caused by challenging environmental conditions including irregular lighting patterns and ambient noise levels in the mining environment with autonomous electric locomotives. A new network model based on SOD−YOLOv5s−4L has been proposed to detect multi‐objects for autonomous electric locomotives in underground coal mines. Improvements have been applied in YOLOv5s to construct the SOD−YOLOv5s−4L model, by introducing the SIoU loss function to address the mismatch between real and predicted bounding box directions, facilitating the model to learn target position information more efficiently. This research introduces a decoupled head to enhance feature fusion and improve the positioning precision of the network model, enabling rapid capture of multi‐scale target features. Furthermore, the detection capability of the model has been increased by introducing the small target detection layer which is developed by increasing the number of detection layers from three to four. The experimental results on multiple object detection dataset show that the proposed model achieves significant improvement in mean average precision (mAP) of almost 98% for various types of targets and an average precision (AP) of nearly 99% for small targets on the other hand it achieves 5.19% (mAP) and 9.79% (AP) compared to the YOLOv5s model. Furthermore, comparative analysis with other models like YOLOv7 and YOLOv8 shows that the proposed model has superior performance in terms of object detection.

Elevators, as essential equipment in modern urban high-rise buildings and in mining industry, rely heavily on traction steel ropes, which represent the elevator's most crucial components. During prolonged usage, issues like wire breakage and strand breakage may occur. If not promptly addressed, these issues can lead to various safety incidents resulting in injuries and property damage. Existing methods for steel rope detection, such as visual inspection and electromagnetic testing, are susceptible to external environmental influences, leading to insufficient stability in detection results. Therefore, this study proposes the use of artificial intelligence algorithms (YOLOv5s) for surface damage detection in steel ropes. Firstly, a dataset for surface damage on steel ropes has been created. Subsequently, the YOLOv5s network model has been trained using the custom dataset. The trained YOLOv5s network has been employed to extract features from images of different types of steel ropes. Experimental results shows that the steel rope surface damage detection model based on the YOLOv5s network exhibits high accuracy, excellent robustness, and fast computation speed, achieving an average detection speed of 6.7 FPS and a testing accuracy of 95.3%. This model provides an effective reference for early warning and alarm systems in elevator incidents, carrying notable practical significance.

Polyvinylidene fluoride (PVDF) is an advanced semicrystalline polymer widely recognized for its unique electroactive properties, including piezoelectric, pyroelectric, and ferroelectric behavior. These properties open a wide range of possibilities for dynamic multifunctional devices designed according to particular demand and requirement such as energy harvesting, precision sensing, actuation system and medical diagnostic applications. In this review we highlight the key aspects of PVDF characterization and fabrication, which explains the detailed its chemical, physical, mechanical, electromechanical and electrical behavior in detail. With the exceptional and unique combination of electroactive high performance, chemical durability and the significant high strength of PVDF polymer, makes it one of the most exceptional and ideal polymer material for various variety of technological and industrial use of applications.