Surface defect inspection is an important task in industrial inspection. Deep
learning-based methods have demonstrated promising performance in this domain.
Nevertheless, these methods still suffer from misjudgment when encountering
challenges such as low-contrast defects and complex backgrounds. To overcome
these issues, we present a decision fusion network (DFNet) that incorporates
the semantic decision with the feature decision to strengthen the decision
ability of the network. In particular, we introduce a decision fusion module
(DFM) that extracts a semantic vector from the semantic decision branch and a
feature vector for the feature decision branch and fuses them to make the final
classification decision. In addition, we propose a perception fine-tuning
module (PFM) that fine-tunes the foreground and background during the
segmentation stage. PFM generates the semantic and feature outputs that are
sent to the classification decision stage. Furthermore, we present an
inner-outer separation weight matrix to address the impact of label edge
uncertainty during segmentation supervision. Our experimental results on the
publicly available datasets including KolektorSDD2 (96.1% AP) and
Magnetic-tile-defect-datasets (94.6% mAP) demonstrate the effectiveness of the
proposed method.

表面缺陷检测是工业检测中的重要任务，本文提出了一个决策融合网络 (DFNet)，通过融合语义决策和特征决策来增强网络的决策能力，同时引入感知微调模块 (PFM) 来对前景和背景进行微调，并通过一个内外分离权重矩阵来解决标签边缘不确定性的影响，在公开数据集上验证了所提方法的有效性。

决策融合网络与感知微调的缺陷分类

Decision Fusion Network with Perception Fine-tuning for Defect  Classification

The performance of face detectors has been largely improved with the
development of convolutional neural network. However, it remains challenging
for face detectors to detect tiny, occluded or blurry faces. Besides, most face
detectors can't locate face's position precisely and can't achieve high
Intersection-over-Union (IoU) scores. We assume that problems inside are
inadequate use of supervision information and imbalance between semantics and
details at all level feature maps in CNN even with Feature Pyramid Networks
(FPN). In this paper, we present a novel single-shot face detection network,
named DF$^2$S$^2$ (Detection with Feature Fusion and Segmentation Supervision),
which introduces a more effective feature fusion pyramid and a more efficient
segmentation branch on ResNet-50 to handle mentioned problems. Specifically,
inspired by FPN and SENet, we apply semantic information from higher-level
feature maps as contextual cues to augment low-level feature maps via a spatial
and channel-wise attention style, preventing details from being covered by too
much semantics and making semantics and details complement each other. We
further propose a semantic segmentation branch to best utilize detection
supervision information meanwhile applying attention mechanism in a
self-supervised manner. The segmentation branch is supervised by weak
segmentation ground-truth (no extra annotation is required) in a hierarchical
manner, deprecated in the inference time so it wouldn't compromise the
inference speed. We evaluate our model on WIDER FACE dataset and achieved
state-of-art results.

本文提出了一种新的单次面部检测网络 DF $^2$ S $^2$，其引入了更有效的特征融合金字塔和在 ResNet-50 上更高效的分割支路来处理细节、遮挡和模糊等问题，并在更高级别的特征映射中应用语义信息作为上下文线索，以增强低级别特征映射，最大限度地利用检测监督信息并在自我监督的方式下，处理语义与细节之间的失衡，并在 WIDER FACE 数据集上取得了最先进的结果。