Abstract
Object detection is one among the major sub-domains of the computer vision which deals with the identification of objects of a pre-defined class. Recognition of object is imminent to identify numerous pertinent objects from an image or video. Several deep neural learning, machine learning based techniques are used for object detection in digital images and videos. This paper discusses a comparative study of some deep learning based object detection frameworks, and analysed on the benchmark mean Average Precision (mAP) and selected models are evaluated using PASCAL VOC 2007 dataset which is the standard image data set for object class identification and recognition. Among the selected detection models, PVANet has the highest mAP (84.9) with FPS 21.7 and is considered as the best object detection method.
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Kurian, E., Mathew, J. (2020). Comparative Study on Deep Learning Frameworks for Object Detection. In: Smys, S., Senjyu, T., Lafata, P. (eds) Second International Conference on Computer Networks and Communication Technologies. ICCNCT 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 44. Springer, Cham. https://doi.org/10.1007/978-3-030-37051-0_9
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