Open AccessHelmet Detection Algorithm Based on Single Pixel Zoom
Author(s) -
Yongheng Zhou,
Lifen Jiang,
Yan Liu,
Chunmei Ma,
Hongzan Sun,
Shuaibao Nie,
Yingxi Zuo
Publication year - 2020
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 - 10.1088/1742-6596/1682/1/012021
Subject(s) - zoom , feature (linguistics) , computer science , artificial intelligence , pixel , object detection , residual , computer vision , task (project management) , object (grammar) , pattern recognition (psychology) , algorithm , engineering , philosophy , linguistics , systems engineering , petroleum engineering , lens (geology)
The automatic detection technology of helmet wearing is of great significance to the safety monitoring of construction site. It is difficult to detect a helmet when the object is small, overlapped, or unclear. Therefore, we propose SPZ-Det model to improve the performance of detect small targets in helmet detection task. We redesign the backbone network layers and proposes a Single Pixel Zoom (SPZ) module. SPZ-Det introduces the lower-level feature map, while for the same scale feature map, we select the higher-level feature map. Then the important elements of each feature map layer are amplified by SPZ module to solve the problem of small object feature fading. In the attention module of SPZ, the residual mechanism is introduced to weaken the influence of the attention. The proposed model improves the detection accuracy and keeps the detection speed. On the Safety-Helmet-Wearing dataset, the mAP of SPZ-Det is 27.9% higher than Efficientdet-d0 and gets an 8.8% increase compares with YOLOv3.