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MTF-NET: A mixed traffic flow multi-target detection network based on full-field perception and adaptive

Shihao Li^1,2, Qiao Meng^1,2, Xin Liu^1,2

¹School of Computer Technology and Application, Qinghai University, Xining, Qinghai, China.

|March 16, 2026

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MTF-NET enhances object detection in mixed traffic by improving feature extraction and addressing small object representation. This novel network achieves superior performance on benchmark datasets, offering a robust solution for complex traffic scenarios.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Mixed traffic presents significant challenges for object detection due to scale variations, occlusions, and diverse classes.
Convolutional Neural Network (CNN) detectors struggle with fixed receptive fields and imbalanced data, limiting performance on small objects.

Purpose of the Study:

To develop an advanced object detection network, MTF-NET, capable of full-field perception for mixed traffic scenarios.
To overcome limitations of existing methods in handling scale disparities, occlusions, and class heterogeneity in complex traffic environments.

Main Methods:

Employed a hybrid CNN and MetaFormer backbone for enhanced contextual modeling in feature extraction.
Introduced a Hierarchical Implicit-Explicit Pyramid and Multi-Kernel Dilation Fusion Network to address information loss and small-target representation.

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Utilized Dynamic Dual Detection Heads and a hybrid loss function (Exponential Adaptive Loss with Focaler-DIoU) to manage sample imbalance and improve detection.

Main Results:

MTF-NET achieved a 5.1% mAP50 improvement on the VisDrone2019 dataset.
Demonstrated significant enhancements of 4.2% and 13.4% on the UA-DETRAC-G2 and HazyDet datasets, respectively.
Outperformed current state-of-the-art methods in mixed traffic object detection tasks.

Conclusions:

MTF-NET exhibits robust performance and strong generalization capabilities in complex mixed traffic flow.
The proposed network offers an effective solution for challenging object detection tasks in real-world traffic scenarios.