Night construction site detection based on ghost-YOLOX

Abstract

Existing target detection algorithms, when applied to night-time site monitoring, are limited by site hardware conditions, lighting conditions, fuzzy and small targets to be detected, etc., which makes it difficult to achieve good detection results. In this paper, we propose the Ghost-YOLOX algorithm for night-time site monitoring using YOLOX-X as a baseline model. In order to reduce the number of model parameters and improve the detection speed, the algorithm is based on the Ghost convolution and SimAM self-attention modules build the Sim-Ghost residual module according to the gradient path design strategy, and uses it to reconstruct the backbone and neck networks; In order to improve the detection ability of the network for fuzzy targets and small targets, based on Involution, a Cross Involution Attention (CIA) module is constructed by double cross convolution, and added to the neck network to enable the network to obtain more efficient channel and spatial attention. The experimental results show that compared with the original YOLOX-X algorithm, the Ghost-YOLOX algorithm reduces the number of parameters to 16.7% of the original model, and the detection speed increases by 2.3 times, at the same time, the average accuracy of the model has increased by 2.55%.

KEYWORDS:

• Nighttime target detection
• Ghost convolution
• involution
• cross convolutional
• attention mechanism

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Shaanxi Provincial Science and Technology Department [grant number: 2023-YBGY-032].