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A Survey on Deep Learning Models Based Road Object Detection Inference
Omar BOUAZIZI ENSA, Abdelmalek Essaadi, Aimad EL MOURABIT ENSA
University Data Engineering & Systems TEAM Tangier Morocco., Abdelmalek Essaadi University Data Engineering & Systems TEAM Tangier Morocco
Abstract: In this paper, we present a comparative study of the object detection accuracy and speed of various state-of-the art models for the road scene context. Commensurate with the model training method, the algorithms can be divided into two types: one-stage models and two-stage models. We focused on the road context in order to detect all occurrences of objects in the road such as, car, person, traffic signs, etc. Accordingly, we find that one-stage detectors are stronger in terms of prediction speed, while two-stage models are stronger in terms of accuracy. To train deep neural networks with platform GPU type on a large amount of image data that required time, Because the computational cost of computer vision is very high, so we are focused to transfer learning technique, where a model trained on one task is reused on another related task, gives better results.
Keywords: Object Detection, Deep Learning, CNN, Transfer Learning, GPU A Survey on Deep Learning Models Based Road Object Detection Inference
DOI:https://doi.org/10.6025/dspaial/2022/1/1/37-41
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