Robust object detection methods in traffic surveillance
scenarios often encounters challenges due to large-scale
deformations and illumination variations in outdoor scenes.
To enhance the tolerance of such methods against these variations,
we design a cross-scale and illumination-invariant detection
model (CSIM) based on the You Only Look Once (YOLO)
architecture. A main cause of false detection in large-scale
detection tasks is the inconsistency between various feature
scales. To address this issue, we introduce an adaptive cross-scale
feature fusion model to ensure the consistency of the constructed
feature pyramid. To overcome the influence of uneven light,
we build an illumination-invariant chromaticity space on the
CSIM model, which is independent of the correlated color
temperature. In addition, we adopt spatial attention modules,
K-means clustering and the Mish activation function for further
model optimization. The obtained experimental results show
that the proposed CSIM produces excellent detection results for
addressing the challenges derived from large-scale deformations
and the illumination changes encountered during traffic surveillance.
Compared with state-of-the-art object detection methods
on public datasets, our proposed model has achieved competitive
results in robust object detection tasks in traffic surveillance
scenarios.