教師資料查詢 | 類別: 會議論文 | 教師: 蔡奇謚 Chi-yi Tsai (瀏覽個人網頁)

標題:Mobilenet-SSDv2: An Improved Object Detection Model for Embedded Systems
學年
學期
發表日期2020/08/31
作品名稱Mobilenet-SSDv2: An Improved Object Detection Model for Embedded Systems
作品名稱(其他語言)
著者Yu-Chen Chiu; Chi-Yi Tsai; Mind-Da Ruan; Guan-Yu Shen; Tsu-Tian Lee
作品所屬單位
出版者
會議名稱International Conference on System Science and Engineering
會議地點Kagawa, Japan
摘要Object detection plays an important role in the field of computer vision. Many superior object detection algorithms have been proposed in literature; however, most of them are designed to improve the detection accuracy. As a result, the requirement of reducing computational complexity is usually ignored. To achieve real-time performance, these superior object detectors need to operate with a high-end GPU. In this paper, we introduce a lightweight object detection model, which is developed based on Mobilenet-v2. The proposed real-time object detector can be applied in embedded systems with limited computational resources. This is one of the key features in the design of modern autonomous driving assistance systems (ADAS). Besides, we also integrate a feature pyramid network (FPN) with the proposed object detection model to effectively improve detection accuracy and detection stability. Experimental results show that the proposed lightweight object detection model achieves up to 75.9% mAP in the VOC dataset. Compared with the existing Mobilenet-SSD detector, the detection accuracy of the proposed detector is improved about 3.5%. In addition, when implemented on the Nvidia Jetson AGX Xavier platform, the proposed detector achieves an average of 19 frames per second (FPS) in processing 720p video streams. Therefore, the proposed lightweight object detector has great application prospects.
關鍵字computational complexity;computer vision;embedded systems;mobile robots;object detection;video streaming
語言英文(美國)
收錄於
會議性質國際
校內研討會地點
研討會時間20200831~20200903
通訊作者
國別日本
公開徵稿
出版型式
出處
相關連結
SDGs
  • 優質教育,產業創新與基礎設施
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