用剪枝版YOLO模型高效检测海洋垃圾，速度快精度高。
Efficient Object Detection of Marine Debris using Pruned YOLO Model
- 对YOLOv4进行通道剪枝，提升检测速度
- 帧率从15.19提升至19.4 FPS，mAP仅降1.2%
- 适合部署在资源受限的水下机器人上
海洋垃圾对海洋生物造成严重危害，其含有的微塑料、多氯联苯和农药会破坏栖息地并毒害生物。人工清理方式已难以应对，因此开发自主水下机器人（AUV）成为趋势，而目标检测架构的选择至关重要。本研究使用YOLOv4模型，在包含7683张图像（480×320像素）的Trash-ICRA 19数据集上实现海洋垃圾的实时检测。通过对比预训练模型、从头训练、马赛克增强、层冻结、YOLOv4-tiny及通道剪枝等方法，发现通道剪枝显著提升效率：基础YOLOv4帧率由15.19 FPS提升至19.4 FPS，平均精度（mAP）仅下降1.2%（从97.6%降至96.4%），验证了该方法在保持高精度前提下的实用性。
原文摘要 · Abstract (English)
Marine debris poses significant harm to marine life due to substances like microplastics, polychlorinated biphenyls, and pesticides, which damage habitats and poison organisms. Human-based solutions, such as diving, are increasingly ineffective in addressing this issue. Autonomous underwater vehicles (AUVs) are being developed for efficient sea garbage collection, with the choice of object detection architecture being critical. This research employs the YOLOv4 model for real-time detection of marine debris using the Trash-ICRA 19 dataset, consisting of 7683 images at 480x320 pixels. Various modifications-pretrained models, training from scratch, mosaic augmentation, layer freezing, YOLOv4-tiny, and channel pruning-are compared to enhance architecture efficiency. Channel pruning significantly improves detection speed, increasing the base YOLOv4 frame rate from 15.19 FPS to 19.4 FPS, with only a 1.2% drop in mean Average Precision, from 97.6% to 96.4%.
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