基于改進MTCNN算法的低功耗邊緣人臉檢測跟蹤系統
2021年電子技術應用第5期
祁星晨，卓旭升
武漢工程大學 電氣信息學院，湖北 武漢430205
摘要： 邊緣設備的快速發展和深度學習的落地應用越來越多，兩者結合的趨勢越發明顯。而針對低功耗邊緣設備AI應用的潛力還未完全開發出來，大量設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濟效益是非常明顯的。因此，以目標檢測任務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，在一定環境條件下，最終成功地檢測到人臉，并繪制出人臉候選框，結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[J].電子技術應用，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[J].電子技術應用，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，邊緣設備等爆炸式增長，百億數量級的邊緣設備接入互聯網。傳統的AI計算架構主要是依靠云計算，雖然云計算能夠提供足夠的計算能力和可靠的計算結果，但其不斷地消耗大量電力，且邊緣設備也需要消耗能量收集數據并傳輸到云端，傳輸過程存在著延遲。而邊緣設備與AI的結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可在邊緣設備完成，降低了云端的負擔，發掘了邊緣設備的計算能力[1-3]。
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作者信息:
祁星晨，卓旭升
(武漢工程大學 電氣信息學院，湖北 武漢430205)
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