期刊论文详细信息
Electronics
Object Identification and Localization Using Grad-CAM++ with Mask Regional Convolution Neural Network
Jer-Guang Hsieh1  Xavier Alphonse Inbaraj2  Charlyn Villavicencio2  Julio Jerison Macrohon2  Jyh-Horng Jeng2 
[1] Department of Electrical Engineering, I-Shou University, Kaohsiung City 84001, Taiwan;Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan;
关键词: Grad-CAM++;    GC-MRCNN;    Mask R-CNN;    object localization;    feature maps;    object detection;   
DOI  :  10.3390/electronics10131541
来源: DOAJ
【 摘 要 】

One of the fundamental advancements in the deployment of object detectors in real-time applications is to improve object recognition against obstruction, obscurity, and noises in images. In addition, object detection is a challenging task since it needs the correct detection of objects from images. Semantic segmentation and localization are an important module to recognizing an object in an image. The object localization method (Grad-CAM++) is mostly used by researchers for object localization, which uses the gradient with a convolution layer to build a localization map for important regions on the image. This paper proposes a method called Combined Grad-CAM++ with the Mask Regional Convolution Neural Network (GC-MRCNN) in order to detect objects in the image and also localization. The major advantage of proposed method is that they outperform all the counterpart methods in the domain and can also be used in unsupervised environments. The proposed detector based on GC-MRCNN provides a robust and feasible ability in detecting and classifying objects exist and their shapes in real time. It is found that the proposed method is able to perform highly effectively and efficiently in a wide range of images and provides higher resolution visual representation than existing methods (Grad-CAM, Grad-CAM++), which was proven by comparing various algorithms.

【 授权许可】

Unknown   

  文献评价指标  
  下载次数:0次 浏览次数:0次