物件偵測、影像辨識和實例分割，是電腦視覺中的主要概念。這種識別和確認影像中
的物件，並將它們分成不同特定類別的過程稱為辨識。另一方面，物件偵測是一種在單張
影像或影片中辨識和標記多個相同物件的方法，它還評估物件的位置和大小。實例分割主
要將傳統的物件偵測任務與影像的語義分割相結合。重量輕、可靠性高的柔性印刷電路板
需要一種高效、快速的缺陷檢測方法，尤其是當缺陷很小且難以手動辨識時。本文提出了
一種基於缺陷類別的柔性印刷電路板（FPC）影像雜質缺陷快速檢測算法，總共有 48 張
已分類的 FPC 影像，經過五種不同的影像處理技術，形成一個包含 1257 張影像的數據集，
並作為輸入。 然後基於 Faster 區域卷積神經網絡 (Faster R-CNN) 物件偵測模型，建立了
FPC 表面缺陷檢測方法。另外，該數據集還與單次檢測器 (SSD)、centernet 和 mobilenet 進
行了比較。通過調整區域候選網路（RPN）中錨點的比例以及特徵提取架構，提高了檢測
精度和計算指標的性能。為了確定所建議的方法是否可行和可靠，比較了所有模型的各種
損失曲線的數值。此外，所建議模型的結果使用 COCO 評估器根據平均精度均值(mAP)
進行評估，即 mAP(@0.50IOU) 和 AR@100 分別為 0.61 和 0.40，與其他模型相比，Faster
r-cnn 模型顯示出最好的結果。每張影像的處理時間為 70 毫秒，與其他方法相比，這是非
常短的時間，本研究中提出的物件偵測模型不僅提高了檢測精度，而且提高了實際效率。

The main broad concepts in computer vision are object detection, image recognition and
instance segmentation. This process of recognizing and confirming objects in an image and parting them into different specific classes is referred to as recognition. Object Detection, on the other hand, is a method of recognizing and labeling a number of identical objects within a single image or in a video frame. It also estimates the object's location and its size. Instance segmentation mostly combines traditional object detection tasks with semantic segmentation from an image. An efficient and quick flaw detection approach is required for flexible printed circuit boards that are lightweight and highly reliable, especially when the defects are small and challenging to identify manually with the naked human eye. Based on the defect categories a rapid detection algorithm of impurity defects in flexible printed circuit (FPC) images isproposed. In this paper, total 48 FPC images with classes, which is further gone through five different image processing techniques to make a dataset of 1257 images, and used as an input. Then, based on the faster regional convolutional neural network (Faster R-CNN) object detection model, we build the FPC surface defect detection method. Also, the data set is compared with single shot detector (SSD), centernet and mobilenet. The detection accuracy and performance of computing the metrics are enhanced by adjusting the ratios of anchor in the region proposal network (RPN) as well as the feature extraction architecture. To determine whether the suggested method is workable and reliable, the value of various loss curves of all models is compared. And also, the suggested model's results are evaluated in terms of mean average precision (mAP) using COCO evaluator i.e. mAP(@0.50IOU) and AR@100 of 0.61 and 0.40 respectively. Faster r-cnn model has shown the best results as compare to other. And each image takes 70ms to process, which is a very brief amount of time when compared to the other approaches. The strategy suggested in this research is not only increases inspection accuracy but also increases practical efficiency.

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