Nighttime vehicle target detection based on visual features

Ling Peng; Libiao Jiang; Ling Peng; Libiao Jiang

doi:10.3934/aci.2026002

Applied Computing and Intelligence

2026, Volume 6, Issue 1: 23-37. doi: 10.3934/aci.2026002

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Research article

Nighttime vehicle target detection based on visual features

Ling Peng ,
Libiao Jiang ^,

School of Mechanical Engineering, Guangzhou City University of Technology, No. 1 Xuefu Road, Huadu District, Guangzhou City, Guangdong Province, China

Academic Editor: Jidong Yang

Received: 07 November 2025 Revised: 23 January 2026 Accepted: 28 January 2026 Published: 04 February 2026

This study proposes an algorithm based on an improved YOLOv8s for nighttime vehicle recognition under low light and complex background conditions, addressing issues such as shallow feature loss, low recognition accuracy for occluded targets, and bounding box distortion. The core optimization strategies are as follows: First, RepGFPN (Re-parameterized Generalized Feature Pyramid Network) employs re-parameterized fusion, utilizing a customized fusion module to achieve multi-scale feature interaction. This preserves shallow-level details while eliminating redundant sampling and adjusting channels to enhance fusion efficiency. Second, the DAT (Deformable Attention Transformer) attention mechanism generates reference grids, while the Lightweight Network predicts deformation point offsets. Bilinear interpolation extracts key features, enhancing learning capabilities for small-sized and occluded targets. Third, the MPDIoU (Minimum Point Distance Intersection over Union) loss function minimizes Euclidean distances between bounding box corners, integrating intersection-over-union optimization to mitigate deformation caused by vehicle overlap. Experimental results demonstrate superior performance over the original model: accuracy improved by 6.4%, recall increased by 4.9%, latency reduced by 6.3 ms, mAP0.5 was boosted by 4.4%, and N_Params decreased by 2.5.
- autonomous vehicles,
- visual features,
- vehicle detection,
- algorithm optimization
Citation: Ling Peng, Libiao Jiang. Nighttime vehicle target detection based on visual features[J]. Applied Computing and Intelligence, 2026, 6(1): 23-37. doi: 10.3934/aci.2026002

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Abstract

This study proposes an algorithm based on an improved YOLOv8s for nighttime vehicle recognition under low light and complex background conditions, addressing issues such as shallow feature loss, low recognition accuracy for occluded targets, and bounding box distortion. The core optimization strategies are as follows: First, RepGFPN (Re-parameterized Generalized Feature Pyramid Network) employs re-parameterized fusion, utilizing a customized fusion module to achieve multi-scale feature interaction. This preserves shallow-level details while eliminating redundant sampling and adjusting channels to enhance fusion efficiency. Second, the DAT (Deformable Attention Transformer) attention mechanism generates reference grids, while the Lightweight Network predicts deformation point offsets. Bilinear interpolation extracts key features, enhancing learning capabilities for small-sized and occluded targets. Third, the MPDIoU (Minimum Point Distance Intersection over Union) loss function minimizes Euclidean distances between bounding box corners, integrating intersection-over-union optimization to mitigate deformation caused by vehicle overlap. Experimental results demonstrate superior performance over the original model: accuracy improved by 6.4%, recall increased by 4.9%, latency reduced by 6.3 ms, mAP0.5 was boosted by 4.4%, and N_Params decreased by 2.5.

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