改进模型精准计数机器零件，误差仅1.96
Counting Machine Parts
- 基于FamNet扩展，加入新损失函数提升计数精度
- 在真实数据集上达1.96的平均绝对误差
- 适合工业质检场景，对遮挡和重叠有强鲁棒性
图像中物体计数是多领域通用任务，如人群、库存、细胞计数。主要挑战包括物体重叠、尺度变化、遮挡及光照差异。本文研究机器洗件零件的计数问题，提出在FamNet基础上增加额外损失组件的方法，在给定数据集上训练。与三种基线方法对比：传统图像处理流程、实例分割和密度图估计。通过计算真实数量与模型输出之间的平均绝对误差（MAE）和均方根误差（RMSE）评估性能。所提方法取得1.96的MAE，显著优于基线。
原文摘要 · Abstract (English)
Counting objects in an image is a task applicable across many domains. For instance, crowd counting, inventory counting, and cell counting have been the focus of recent research. The major challenges in estimating the count of objects include overlapping objects, object scale issues, occlusions, and varying lighting conditions. In this report, we explore the problem of counting machine washer parts. Our technique is an extension of FamNet with an additional loss component, trained on the given dataset. We compare to three baseline methods: a traditional image processing pipeline, instance segmentation, and density map estimation. We evaluate the performance of these algorithms by computing the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) between the true object counts and the model outputs. Our approach achieves a performance of 1.96 MAE.
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