基于改進MTCNN算法的低功耗邊緣人臉檢測跟蹤系統(tǒng)
2021年電子技術(shù)應(yīng)用第5期
祁星晨，卓旭升
武漢工程大學(xué) 電氣信息學(xué)院，湖北 武漢430205
摘要： 邊緣設(shè)備的快速發(fā)展和深度學(xué)習(xí)的落地應(yīng)用越來越多，兩者結(jié)合的趨勢越發(fā)明顯。而針對低功耗邊緣設(shè)備AI應(yīng)用的潛力還未完全開發(fā)出來，大量設(shè)備隱藏著大量計算能力，釋放其潛力所帶來的社會效益和經(jīng)濟效益是非常明顯的。因此，以目標檢測任務(wù)中較為常見的人臉檢測為例，將MTCNN人臉檢測算法改進并移植到資源極其緊張的低功耗嵌入式平臺，在一定環(huán)境條件下，最終成功地檢測到人臉，并繪制出人臉候選框，結(jié)合舵機云臺具備了一定的人臉跟蹤能力。
中圖分類號： TP391
文獻標識碼： A
DOI：10.16157/j.issn.0258-7998.201100
中文引用格式： 祁星晨，卓旭升. 基于改進MTCNN算法的低功耗邊緣人臉檢測跟蹤系統(tǒng)[J].電子技術(shù)應(yīng)用，2021，47(5)：40-44.
英文引用格式： Qi Xingchen，Zhuo Xusheng. Low-power edge AI face detection and tracking system based on improved MTCNN algorithm[J]. Application of Electronic Technique，2021，47(5)：40-44.
文獻標識碼： A
DOI：10.16157/j.issn.0258-7998.201100
中文引用格式： 祁星晨，卓旭升. 基于改進MTCNN算法的低功耗邊緣人臉檢測跟蹤系統(tǒng)[J].電子技術(shù)應(yīng)用，2021，47(5)：40-44.
英文引用格式： Qi Xingchen，Zhuo Xusheng. Low-power edge AI face detection and tracking system based on improved MTCNN algorithm[J]. Application of Electronic Technique，2021，47(5)：40-44.
Low-power edge AI face detection and tracking system based on improved MTCNN algorithm
Qi Xingchen，Zhuo Xusheng
School of Information and Electrical Engineering，Wuhan Institute of Technology，Wuhan 430205，China
Abstract： The rapid development of edge devices and the application of deep learning are increasing, the trend of combining the two is becoming more and more obvious. The potential of AI applications for low-power edge devices has not yet been fully developed. A large number of devices hide a lot of computing power. The social and economic benefits brought by the release of its potential are very obvious. Therefore, taking the more common face detection in objective detection tasks as an example, the MTCNN face detection algorithm is improved and transplanted to a low-power embedded platform with extremely limited resources. Under certain environmental conditions, the face is finally successfully detected,and the face candidate boundingbox is drawn, it has face tracking function combined with the servo.
Key words : low-power edge devices；object detection；face detection and tracking；cascaded convolutional neural network
0 引言
近年來，邊緣設(shè)備等爆炸式增長，百億數(shù)量級的邊緣設(shè)備接入互聯(lián)網(wǎng)。傳統(tǒng)的AI計算架構(gòu)主要是依靠云計算，雖然云計算能夠提供足夠的計算能力和可靠的計算結(jié)果，但其不斷地消耗大量電力，且邊緣設(shè)備也需要消耗能量收集數(shù)據(jù)并傳輸?shù)皆贫耍瑐鬏斶^程存在著延遲。而邊緣設(shè)備與AI的結(jié)合能夠降低能源的消耗以及降低延遲，使得原本在云端完成的任務(wù)可在邊緣設(shè)備完成，降低了云端的負擔，發(fā)掘了邊緣設(shè)備的計算能力[1-3]。
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作者信息:
祁星晨，卓旭升
(武漢工程大學(xué) 電氣信息學(xué)院，湖北 武漢430205)
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