Open AccessYolo-tiny-MSC: A tiny neural network for object detection
Author(s) -
Dongsheng Li,
Yujie Zhang,
Junping Xiang,
Jianfei Li,
Yu Zeng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1873/1/012073
Subject(s) - object detection , computer science , object (grammar) , cascade , artificial intelligence , artificial neural network , resource (disambiguation) , computer vision , distributed computing , real time computing , pattern recognition (psychology) , engineering , computer network , chemical engineering
Object detection is an important and active area of computer vision. However, the expensive computational cost and memory requirement are big challenges for deploying object detection networks in resource constraints embedded devices. To address this problem, we designed the multi-stage cascade module to reduce the computational cost of object detection networks. The proposed structure is used to build a compact and efficient object detection neural network. Experiments show that our multi-stage cascade module could significantly reduce the computational and parameter budgets meanwhile achieve outstanding object detection accuracy.