發(fā)布時間：2018-03-20 02:12

  本文選題：智能洗衣機　切入點：深度學習　出處：《深圳大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著社會的進步和科技的發(fā)展,家電產(chǎn)品得到了廣泛的普及。而計算機技術(shù),包括深度學習技術(shù)的迅猛發(fā)展,使得人們對家電的智能化需求成為可能。將深度學習技術(shù)應用在智能家電領(lǐng)域?qū)⑹侵悄芗译婎I(lǐng)域新的發(fā)展方向。深度學習算法可以克服傳統(tǒng)智能家電的局限,使智能家電為人們帶來更加便捷和舒適的生活模式。本文主要提出了一種新型智能洗衣機的設(shè)計模式,在洗衣機內(nèi)部放置一個高清攝像頭,通過該攝像頭采集到待洗衣物的圖像,然后利用神經(jīng)網(wǎng)絡(luò)算法對圖像進行分析,得出洗衣機內(nèi)部衣物量,衣物的材質(zhì)信息,例如毛衣類衣物,牛仔面料衣物以及普通棉麻類衣物。根據(jù)這些信息自動提供一套合適當前環(huán)境下的洗衣方案,如洗衣過程需要的水量,需要的洗滌劑量,洗滌劑的種類以及洗衣過程波輪旋轉(zhuǎn)的速率等,從而智能控制洗衣機。本文的創(chuàng)新點及主要工作在于:1)提出一個結(jié)合深度學習和智能洗衣機的具體應用場景,并描述了其整套工作流程,同時將問題轉(zhuǎn)化成圖像分割和紋理圖像分類領(lǐng)域的問題;2)設(shè)計了一個基于卷積神經(jīng)網(wǎng)絡(luò)的圖像分割算法,該算法可以實現(xiàn)對灰度圖像的前景背景分離;設(shè)計了一個基于卷積神經(jīng)網(wǎng)絡(luò)的紋理圖像分類算法,該算法能夠識別毛衣面料紋理、普通棉麻面料紋理以及牛仔面料紋理;以及一個基于淺層學習的圖像分類模型,該模型能夠識別掩碼的面積大小;3)實現(xiàn)了一個基于上述算法應用于智能洗衣機系統(tǒng)的仿真,并證實了可以通過深度學習算法實現(xiàn)智能洗衣機對內(nèi)部衣物的衣量和衣物材質(zhì)識別。本文分別從網(wǎng)絡(luò)結(jié)構(gòu)、激活函數(shù)、損失函數(shù)、優(yōu)化算法以及過擬合防范方法5個方面描述了算法的設(shè)計和實現(xiàn),并通過實驗驗證了通過神經(jīng)網(wǎng)絡(luò)算法能夠獲取傳感器所無法獲取的衣物材質(zhì)信息。因此這種新型智能洗衣機能夠制定更加合理的洗衣方案,避免用戶在使用洗衣機的過程中做太多基于經(jīng)驗的選擇,為用戶提供更加智能的洗衣模式、帶來更加便捷生活方式。
[Abstract]:With the progress of society and the development of science and technology, household appliances have been widely popularized. And the rapid development of computer technology, including in-depth learning technology, It will be a new development direction to apply the deep learning technology to the intelligent appliance field. The depth learning algorithm can overcome the limitation of the traditional intelligent home appliance. This paper puts forward a new design mode of intelligent washing machine, in which a high-definition camera is placed inside the washing machine. Through the camera to collect the image of laundry, and then use the neural network algorithm to analyze the image, get the washing machine inside the amount of clothing, clothing material information, such as sweater clothing, Denim fabrics and general cotton and linen clothing. Based on this information, an automated laundry program is provided for the current environment, such as the amount of water needed for the laundry process, the amount of washing needed, The type of detergent and the speed of washing wheel rotation in the laundry process, so as to control the washing machine intelligently. The innovation and main work of this paper is to put forward a concrete application scene combining deep learning with intelligent washing machine. At the same time, the problem is transformed into the problem of image segmentation and texture image classification. (2) an image segmentation algorithm based on convolution neural network is designed. A texture classification algorithm based on convolution neural network is designed, which can recognize the texture of sweater fabric, common cotton fabric and denim fabric. And an image classification model based on shallow learning. The model can recognize the area of mask and realize a simulation of intelligent washing machine system based on the above algorithm. It is proved that the intelligent washing machine can realize the recognition of the clothing quantity and clothing material through the depth learning algorithm. In this paper, the network structure, the activation function, the loss function, the structure, the activation function and the loss function, respectively, can be realized. The design and implementation of the algorithm are described in five aspects: optimization algorithm and over-fitting prevention method. The experiment proves that the new intelligent washing machine can make a more reasonable laundry scheme, which can not be obtained by the sensor through the neural network algorithm. Avoid users making too many experience-based choices in the process of using washing machines, provide users with a more intelligent laundry mode, and bring a more convenient way of life.
【學位授予單位】：深圳大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TM925.33

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