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Research on marine litter detection based on CNN-Transformer heterogeneous parallelism
Summary
Researchers developed YOLO-Trans, a CNN-Transformer hybrid model for detecting marine debris in ocean imagery, outperforming standard detection models on a multi-scenario dataset of small and deformed targets. Accurate automated marine litter detection is critical for monitoring and quantifying plastic pollution at sea, enabling more effective large-scale cleanup and pollution tracking.
Aiming at strong background interference and low detection accuracy of small/deformed targets in marine debris detection, this paper develops a high-precision lightweight intelligent detection and recognition system. A multi-scenario dataset is built and data augmentation is used to tackle sample scarcity and domain shift; a CNN-Transformer heterogeneous parallel model YOLO-Trans is designed on the YOLOv8 baseline for local-to-global feature extraction and accurate detection of small/deformed targets, and a visual detection system is developed with PyQt5. Experiments show the model surpasses the original YOLOv8s in all metrics on the self-built dataset, and ablation experiments validate the improved modules’ effectiveness, offering technical support for large-scale intelligent marine debris monitoring.