本文關(guān)鍵詞： 喚醒機(jī)制 傳感器網(wǎng)絡(luò) 哈希算法 LSH圖像檢索 賦值權(quán)重　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近些年來,隨著信息技術(shù)的不斷進(jìn)步,基于圖像的無線傳感器網(wǎng)絡(luò)應(yīng)用技術(shù)成為研究熱點(diǎn)。在圖像、視頻等大數(shù)據(jù)傳輸中,傳輸數(shù)據(jù)所付出的能量代價(jià)將大幅提高,傳統(tǒng)休眠調(diào)度機(jī)制所減少的能量消耗將不再變得有效,如何保證傳感器最大限度減少傳輸消耗又能夠同時(shí)滿足有價(jià)值圖像數(shù)據(jù)及時(shí)有效的傳輸至SINK節(jié)點(diǎn)成為本文的研究重點(diǎn),本文以重點(diǎn)區(qū)域監(jiān)測(cè)為應(yīng)用背景,研究無線傳感器網(wǎng)絡(luò)應(yīng)用中基于圖像檢索技術(shù)的自主喚醒機(jī)制。傳統(tǒng)的休眠辦法是人為地對(duì)傳感器網(wǎng)絡(luò)進(jìn)行休眠干預(yù)以達(dá)到減少傳輸消耗的目的,檢索喚醒的辦法是由傳感器網(wǎng)絡(luò)本身對(duì)數(shù)據(jù)進(jìn)行自主判斷,對(duì)采集圖像進(jìn)行篩選,減少數(shù)據(jù)量從而達(dá)到降低能耗的目的。本文提出的基于圖像檢索的傳感器喚醒機(jī)制提供了一種新的技術(shù)應(yīng)用,將深度學(xué)習(xí)與機(jī)器視覺技術(shù)應(yīng)用于無線傳感器網(wǎng)絡(luò),主要工作內(nèi)容如下:首先,研究了哈希算法,提出傳感器節(jié)點(diǎn)初步喚醒機(jī)制,節(jié)點(diǎn)采集的數(shù)據(jù)經(jīng)處理后轉(zhuǎn)變?yōu)楣Ｐ蛄?將其與背景圖像哈希序列進(jìn)行相似度匹配來初步判定是否有目標(biāo)出現(xiàn),判定目標(biāo)出現(xiàn)后,傳感器節(jié)點(diǎn)喚醒通信傳輸模塊,將哈希指紋序列傳輸至SINK節(jié)點(diǎn)。其次,研究了圖像差分技術(shù)以及閾值分割技術(shù),完成傳感器節(jié)點(diǎn)采集的圖像數(shù)據(jù)中對(duì)目標(biāo)區(qū)域與背景的分割,通過3DMAX軟件模擬目標(biāo)各姿態(tài)和場(chǎng)景,完成對(duì)目標(biāo)不同姿態(tài)和場(chǎng)景下的圖像庫(kù)的建立,研究圖像特征提取技術(shù),建立基于目標(biāo)圖像庫(kù)及Cifar-10圖像庫(kù)的GIST特征庫(kù)。第三,研究圖像索引技術(shù),重點(diǎn)研究LSH圖像檢索算法,明確評(píng)價(jià)標(biāo)準(zhǔn),在此基礎(chǔ)上通過訓(xùn)練,提出基于賦值權(quán)重的WLSH檢索訓(xùn)練算法,提高目標(biāo)識(shí)別精度。最后,完成對(duì)以上三部分內(nèi)容的仿真分析。
[Abstract]:In recent years, with the development of information technology, the application technology of image-based wireless sensor network (WSNs) has become a research hotspot. In the transmission of images, video and other big data, the energy cost of transmitting data will be greatly increased. The energy consumption reduced by the traditional sleep scheduling mechanism will no longer become effective. How to ensure that the sensor minimizes transmission consumption and can simultaneously meet the needs of timely and effective transmission of valuable image data to the SINK node becomes the focus of this paper. The background of this paper is the monitoring of key areas. This paper studies the automatic wake-up mechanism based on image retrieval technology in wireless sensor network application. The traditional sleep method is to intervene in sensor network sleep artificially in order to reduce transmission consumption. The method of retrieving wake-up is for the sensor network itself to judge the data independently and screen the collected images. In this paper, the sensor wake-up mechanism based on image retrieval provides a new technology application, which applies depth learning and machine vision technology to wireless sensor networks. The main work is as follows: firstly, the hashing algorithm is studied, and the initial wake-up mechanism of sensor node is proposed. The data collected by the sensor node is transformed into a hash sequence after processing. Matching the similarity with the background image hash sequence to determine whether the target appears or not, the sensor node wake up the communication transmission module, and transmit the hash fingerprint sequence to the SINK node. Secondly, after the target appears, the sensor node awakens the communication transmission module, and transmits the hash fingerprint sequence to the SINK node. The image difference technology and threshold segmentation technology are studied to complete the segmentation of the target region and background in the image data collected by sensor nodes, and the 3D Max software is used to simulate each pose and scene of the target. Complete the establishment of image database under different pose and scene of target, study the technology of image feature extraction, establish the GIST signature database based on target image database and Cifar-10 image library. Thirdly, study the image index technology, and focus on the LSH image retrieval algorithm. On the basis of training, the WLSH retrieval training algorithm based on assignment weight is proposed to improve the accuracy of target recognition. Finally, the simulation analysis of the above three parts is completed.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212.9;TN929.5;TP391.41

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本文編號(hào)：1519765