A Systematic Framework for Exploring Valley Fracture Failure Mechanisms During a River Downcutting Process: A Case Study from the Hengduan Mountains

Rock Mechanics and Rock Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 8, 2025

Language: Английский

Lightweight obstacle detection for unmanned mining trucks in open-pit mines

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 16, 2025

This paper aims to solve the problem of difficulty in balancing model size and detection accuracy unmanned mining truck network open-pit mines, as well that existing is not suitable for equipment. To address this problem, we proposed a lightweight vehicle algorithm based on improvement YOLOv8. Through series innovative structural adjustments optimization strategies, has achieved high low complexity. replaces backbone YOLOv8s with FasterNet_t0 (FN) network. advantages simple structure lightweight, which effectively reduces amount calculation parameters model. Then feature extraction YOLOv8 neck replaced BiFPN (Bi-directional Feature Pyramid Network) structure. By increasing cross-layer connections removing nodes contribution fusion, fusion utilization features different scales are optimized, performance further improved, number calculations reduced. make up possible loss caused by improvements, head Dynamic Head. design can introduce self-attention mechanism from three dimensions scale, space, task, significantly improving while avoiding additional computational burden. In terms function, introduces combination SIoU NWD (normalized Gaussian Wasserstein distance) loss. These two enable cope scenarios more accurately, especially effect small target trucks improved. addition, also adopts amplitude-based layer adaptive sparse pruning (LAMP) compress maintaining efficient performance. strategy, its dependence computing resources key experimental part, dataset 3000 images was first constructed, these were preprocessed, including image enhancement, denoising, cropping, scaling. The environment set Autodl cloud server, using PyTorch 2.5.1 framework Python 3.10 environment. four sets ablation experiments, verified specific impact each results show strategy improves model, greatly reducing Finally, conducted comprehensive comparative analysis improved other popular algorithms models. our leads 76.9%, than 10% higher similar At same time, compared models achieve levels, only about 20% size. fully prove adopted feasible obvious efficiency.

Language: Английский

ASDS-you only look once version 8: A real-time segmentation method for cross-scale prefabricated laminated slab components

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 153, P. 110958 - 110958

Published: April 26, 2025

Language: Английский