Vehicle and Pedestrian Detection Based on Multi-Level Feature Fusion in Autonomous Driving

Title:Vehicle and Pedestrian Detection Based on Multi-Level Feature Fusion in Autonomous Driving

Volume: 14 Issue: 7

Author(s): Chen Guoqiang*, Yi Huailong and Mao Zhuangzhuang

Affiliation:

• School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454000,China

Keywords: Vehicle and pedestrian detection, convolutional neural network, autonomous driving, multi-level feature fusion, deep learning, algorithm.

Abstract:

Aims: The factors including light, weather, dynamic objects, seasonal effects and structures bring great challenges for the autonomous driving algorithm in the real world. Autonomous vehicles can detect different object obstacles in complex scenes to ensure safe driving.

Background: The ability to detect vehicles and pedestrians is critical to the safe driving of autonomous vehicles. Automated vehicle vision systems must handle extremely wide and challenging scenarios.

Objective: The goal of the work is to design a robust detector to detect vehicles and pedestrians. The main contribution is that the Multi-level Feature Fusion Block (MFFB) and the Detector Cascade Block (DCB) are designed. The multi-level feature fusion and multi-step prediction are used which greatly improve the detection object precision.

Methods: The paper proposes a vehicle and pedestrian object detector, which is an end-to-end deep convolutional neural network. The key parts of the paper are to design the Multi-level Feature Fusion Block (MFFB) and Detector Cascade Block (DCB). The former combines inherent multi-level features by combining contextual information with useful multi-level features that combine high resolution but low semantics and low resolution but high semantic features. The latter uses multistep prediction, cascades a series of detectors, and combines predictions of multiple feature maps to handle objects of different sizes.

Results: The experiments on the RobotCar dataset and the KITTI dataset show that our algorithm can achieve high precision results through real-time detection. The algorithm achieves 84.61% mAP on the RobotCar dataset and is evaluated on the well-known KITTI benchmark dataset, achieving 81.54% mAP. In particular, the detection accuracy of a single-category vehicle reaches 90.02%.

Conclusion: The experimental results show that the proposed algorithm has a good trade-off between detection accuracy and detection speed, which is beyond the current state-of-the-art Refine- Det algorithm. The 2D object detector is proposed in the paper, which can solve the problem of vehicle and pedestrian detection and improve the accuracy, robustness and generalization ability in autonomous driving.

Cite this article as:

Guoqiang Chen *, Huailong Yi and Zhuangzhuang Mao , Vehicle and Pedestrian Detection Based on Multi-Level Feature Fusion in Autonomous Driving, Recent Advances in Computer Science and Communications 2021; 14 (7) . https://dx.doi.org/10.2174/2666255813666200304123323