Resource-efficient fine-tuning of large vision-language models for multimodal perception in autonomous excavators

Recent advances in large vision-language models (LVLMs) have transformed visual recognition research by enabling multimodal integration of images, text, and videos. This fusion supports a deeper and more context-aware understanding of visual environments. However, the application of LVLMs to multitask visual recognition in real-world construction scenarios remains underexplored. In this study, we present a resource-efficient framework for fine-tuning LVLMs tailored to autonomous excavator operations, with a focus on robust detection of humans and obstacles, as well as classification of weather conditions on consumer-grade hardware. By leveraging Quantized Low-Rank Adaptation (QLoRA) in conjunction with the Unsloth framework, our method substantially reduces memory consumption and accelerates fine-tuning compared with conventional approaches. We comprehensively evaluate a domain-specific excavator-vision dataset using five open-source LVLMs. These include Llama-3.2-Vision, Qwen2-VL, Qwen2.5-VL, LLaVA-1.6, and Gemma 3. Each model is fine-tuned on 1,000 annotated frames and tested on 2000 images. Experimental results demonstrate significant improvements in both object detection and weather classification, with Qwen2-VL-7B achieving an mAP@50 of 88.03%, mAP@[0.50:0.95] of 74.20%, accuracy of 84.54%, and F1 score of 78.83%. Our fine-tuned Qwen2-VL-7B model not only detects humans and obstacles robustly but also classifies weather accurately. These results illustrate the feasibility of deploying LVLM-based multimodal AI agents for safety monitoring, pose estimation, activity tracking, and strategic planning in autonomous excavator operations.