近年來，深度學習和機器學習已經被大家所關注，其中以卷積神經網路 (Convolution Neural Network, CNN)在影像辨識中相較於傳統分類方法有突 破性的表現。其中影像辨識中的物件偵測可以應用在生活中的許多地方，包 含行人偵測、人臉辨識、無人商店以及無人車的應用。物件偵測的其中一個 網路架構為 SSD : Single Shot MultiBox Detector，其特色為結合多尺度特徵 的物件偵測方法，有效提升準確度。本論文使用了兩種網路的優勢，其一為 多尺度網路，另一個為特徵金字塔，提出了在網路加入了注意力機制，本論 文提出方法為端對端(end-to-end)訓練的網路架構。 本論文是以多尺度金字塔模型(FPNSSD)為基礎，並且加入了注意力機 制。注意力機制在殘差注意力分類(Residual Attention Classification)已經實 現了分類上的注意力，並且對分類準確度有相當的提升，因此本論文在 FPNSSD 中加入注意力機制，且繼承原本網路可以多尺度偵測的特性，使得 在辨識的時候，對於小物件比較能抓出它的關鍵特徵，能使小物件的準確度 提升。 在實驗上，我們在VOC2012 的測試集上做實驗，實驗結果顯示加入注 意力機制的網路對於小物件，例如鳥類、瓶子等小物件有較高準確度。;In recent years, deep learning plays an important role in Artificial Intelligence, which Convolutional Neural Network(CNN) has a breakthrough performance comparing with the traditional methods in image classification. Object detection is the popular issue in the image processing, and it has a lot of applications in our life, include face detection, pedestrian detection which can be used in self-driving car and the self-service store need the object detection application in product detection. There were lots of object detection research published in the world. One is SSD: Single Shot Multibox Detector, which combines predictions from multiple feature maps with different resolutions to naturally handle objects of various size. Our paper combines the advantages of two networks: multi-scale network and feature pyramid network. Proposed adding the attention mechanism to the network. This network can be trained end-to-end. In this work, based on FPNSSD network and add Attention mechanism into multi-scale network. The Attention mechanism can let the deep network learned the important area in the feature map, and gave more weight in important area. Because the attention mechanism had better performance in classification and segmentation, we add attention in the multi-scale network, hopes it have better performance in small object detection. In the experiment, FPNSSD with attention got the better performance of bonding box and classification in the small object like bird, bottle in VOC challenge 2012.
