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圖像識(shí)別算法工程師求職簡(jiǎn)歷模板使用便捷-高效-實(shí)用，高效提升求職效率，做高質(zhì)量簡(jiǎn)歷就用錘子簡(jiǎn)歷。

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? 頭像: 隱藏

編輯

["sex","age","nation","education","marriageStatus","politicalStatus","city","jobYear","mobile","email"]

? 基本信息: 女

28歲



本科

未婚

中共黨員

?

4年經(jīng)驗(yàn)

13800138000

BD@100chui.com

? 姓名: 錘子簡(jiǎn)歷

夢(mèng)想每個(gè)人都有，但不是每個(gè)人都有勇氣去堅(jiān)信，我有!

 求職意向: 
圖像識(shí)別算法工程師


廣州


面議


隨時(shí)到崗

 教育背景: 2014.09 - 2018.06

錘子簡(jiǎn)歷大學(xué)

計(jì)算機(jī)與信息技術(shù)

GPA：3.72/4（專(zhuān)業(yè)前10%）
GRE：324

 工作經(jīng)驗(yàn)

2019年12月 - 至今

錘子簡(jiǎn)歷信息有限公司

圖像識(shí)別算法工程師

使用C++/MFC進(jìn)行無(wú)人機(jī)光伏電站軟件系統(tǒng)的構(gòu)建面板定位模塊使用TensorFlow以及ssdmobilenet訓(xùn)練的模型來(lái)實(shí)現(xiàn);
面板精準(zhǔn)分割和定位使用OpenCV進(jìn)行開(kāi)發(fā)，算法需要在山地，水面，沙漠等多種極端環(huán)境下實(shí)現(xiàn)對(duì)電站面板的精準(zhǔn)定位，所以對(duì)于算法的魯棒性要求較高;
主要使用OpenCV和C++來(lái)實(shí)現(xiàn)算法的設(shè)計(jì)算法是用來(lái)進(jìn)行鋰電池的檢測(cè)，實(shí)現(xiàn)正極和負(fù)極的抓點(diǎn)準(zhǔn)確，進(jìn)而達(dá)到檢測(cè)鋰電池是否合格的目的;
使用及改進(jìn)MTCNN算法進(jìn)行人臉檢測(cè)，并使用自有數(shù)據(jù)訓(xùn)練模型;
使用Dlib的ERT集成回歸樹(shù)算法進(jìn)行68點(diǎn)關(guān)鍵點(diǎn)檢測(cè)，并使用自有數(shù)據(jù)訓(xùn)練95點(diǎn)數(shù)據(jù)模型;
使用Dlib、Facenet進(jìn)行人臉識(shí)別，爬蟲(chóng)，根據(jù)需求制定爬取策略，抓取數(shù)據(jù);

2018年3月 - 2019年12月

錘子簡(jiǎn)歷科技有限公司

圖像識(shí)別算法工程師

負(fù)責(zé)收集公司內(nèi)部的需求，實(shí)時(shí)從平臺(tái)獲取圖像，并根據(jù)需求對(duì)圖像進(jìn)行處理和分析，對(duì)客戶提出關(guān)于圖像處理和計(jì)算機(jī)視覺(jué)方面相關(guān)對(duì)問(wèn)題，給予解決方案;
與項(xiàng)目對(duì)其他成員合作，協(xié)助完成相關(guān)的測(cè)試，在該工作主要做過(guò)對(duì)項(xiàng)目有全自動(dòng)等檢機(jī)圖像識(shí)別系統(tǒng)，人臉識(shí)別系統(tǒng);
負(fù)責(zé)學(xué)生試卷手寫(xiě)的答題和老師手動(dòng)批改的識(shí)別，和產(chǎn)品部門(mén)的需求對(duì)接與技術(shù)可行性分析；
負(fù)責(zé)訓(xùn)練數(shù)據(jù)的采集和篩選以及預(yù)處理，模型的設(shè)計(jì)訓(xùn)練與調(diào)優(yōu);

 項(xiàng)目經(jīng)驗(yàn)

2021年3月 - 2021年11月

項(xiàng)目工程

圖像識(shí)別算法工程師

項(xiàng)目描述：該項(xiàng)目主要分為兩部分，人間檢測(cè)，人臉識(shí)別，人臉檢測(cè)主要用mtcnn來(lái)提取到人臉框，人臉識(shí)別主要計(jì)算圖片之間的歐幾里得距離來(lái)判斷。
責(zé)任描述：在該項(xiàng)目中主要運(yùn)用了MTCNN來(lái)進(jìn)行人間檢測(cè)，運(yùn)用了FaceNet來(lái)進(jìn)行人臉識(shí)別。在輸入層使用圖像金字塔進(jìn)行初始圖像的尺度變換，并使用P-Net生成大量的候選目標(biāo)區(qū)域框，之后使用R-Net對(duì)這些目標(biāo)區(qū)域框進(jìn)行第一次精選和邊框回歸，排除大部分的負(fù)例，然后再用更復(fù)雜的、精度更高的網(wǎng)絡(luò)O-Net對(duì)剩余的目標(biāo)區(qū)域框進(jìn)行判別和區(qū)域邊框回歸。
通過(guò)CNN將人臉映射到歐式空間的特征向量上，計(jì)算不同圖片人臉特征的距離，通過(guò)相同個(gè)體的人臉的距離，總是小于不同個(gè)體的人臉這一先驗(yàn)知識(shí)訓(xùn)練網(wǎng)絡(luò)。
測(cè)試時(shí)只需要計(jì)算人臉特征，然后計(jì)算距離使用閾值即可判定兩張人臉照片是否屬于相同的個(gè)體。

 自我評(píng)價(jià): 本人對(duì)待工作踏實(shí)，認(rèn)真，并且極富工作和團(tuán)隊(duì)精神，因此在工作和生活中結(jié)交了許多朋友，具有良好的適應(yīng)性和熟練的溝通技巧，相信能夠協(xié)助主管人員出色地完成各項(xiàng)工作。感謝您在百忙之中閱覽我的簡(jiǎn)歷，靜候佳音！