ArticleScientific reports2026
Conveyor belt foreign object detection method based on improved YOLOv11 and ESRGAN.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Traditional conveyor belt object detection methods often lack robustness and adaptability under challenging conditions such as low-light and low-resolution environments. This study proposes an improved detection method specifically designed for conveyor belt environments, built upon the YOLOv11 object detection framework. A custom dataset was created to support foreign object detection on factory conveyor belts. To overcome the low resolution of image recognition, the Enhanced Super- Resolution Generative Adversarial Network (ESRGAN) was employed to improve the input image clarity. Additionally, to enhance the performance under low-illumination conditions, several architectural improvements were embedded in the YOLOv11 framework, leading to the proposed Conveyor Belt Foreign Object Detection (YOLOv11-CBFD) algorithm. These enhancements included an optimized upsampling module, integrated attention mechanisms, a modified convolution module, an improved loss function, and a modified convolution module. Experimental results demonstrated that the proposed YOLOv11-CBFD algorithm significantly enhanced the accuracy of foreign object recognition. Based on a dataset collected from a factory conveyor belt, YOLOv11-CBFD achieved an accuracy of 86.1%, a recall of 86.7%, an [Formula: see text] of 89.1%, and a model size of only 2.17 M parameters. Compared to the original YOLOv11n model, the proposed method reduced the parameter count by 16.2% while demonstrating no significant degradation in recognition capabilities. In terms of computational efficiency, the optimized architecture demonstrated a 12.4% increase in the number of frames per second when deployed on a Jetson Orin NX-embedded AI computer. Field experiments conducted in industrial inspection scenarios validated the practical effectiveness of the system, demonstrating continuous operation over 48 h under real-time constraints (average latency <33 ms/frame), while consistently maintaining an accuracy of 86.1% across multiple deployment cycles. The experimental results highlight the ability of the model to effectively balance computational efficiency and detection performance on embedded AI platforms.
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