High-precision real-time autonomous driving target detection based on YOLOv8

IF 2.9 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Huixin Liu, Guohua Lu, Mingxi Li, Weihua Su, Ziyi Liu, Xu Dang, Dongyuan Zang
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Abstract

In traffic scenarios, the size of targets varies significantly, and there is a limitation on computing power. This poses a significant challenge for algorithms to detect traffic targets accurately. This paper proposes a new traffic target detection method that balances accuracy and real-time performance—Deep and Filtered You Only Look Once (DF-YOLO). In response to the challenges posed by significant differences in target scales within complex scenes, we designed the Deep and Filtered Path Aggregation Network (DF-PAN). This module effectively fuses multi-scale features, enhancing the model's capability to detect multi-scale targets accurately. In response to the challenge posed by limited computational resources, we design a parameter-sharing detection head (PSD) and use Faster Neural Network (FasterNet) as the backbone network. PSD reduces computational load by parameter sharing and allows for feature extraction capability sharing across different positions. FasterNet enhances memory access efficiency, thereby maximizing computational resource utilization. The experimental results on the KITTI dataset show that our method achieves satisfactory balances between real-time and precision and reaches 90.9% mean average precision(mAP) with 77 frames/s, and the number of parameters is reduced by 28.1% and the detection accuracy is increased by 3% compared to the baseline model. We test it on the challenging BDD100K dataset and the SODA10M dataset, and the results show that DF-YOLO has excellent generalization ability.

Abstract Image

基于 YOLOv8 的高精度实时自动驾驶目标检测
在交通场景中,目标的大小差异很大,而且计算能力有限。这给算法准确检测交通目标带来了巨大挑战。本文提出了一种兼顾准确性和实时性的新型交通目标检测方法--深度过滤只看一次(DF-YOLO)。为了应对复杂场景中目标尺度的显著差异所带来的挑战,我们设计了深度和过滤路径聚合网络(DF-PAN)。该模块可有效融合多尺度特征,增强模型准确检测多尺度目标的能力。为应对有限计算资源带来的挑战,我们设计了参数共享检测头(PSD),并使用快速神经网络(FasterNet)作为骨干网络。PSD 通过参数共享降低了计算负荷,并允许不同位置共享特征提取能力。FasterNet 提高了内存访问效率,从而最大限度地提高了计算资源利用率。在 KITTI 数据集上的实验结果表明,我们的方法在实时性和精确度之间取得了令人满意的平衡,以 77 帧/秒的速度达到了 90.9% 的平均精确度(mAP),与基线模型相比,参数数量减少了 28.1%,检测精确度提高了 3%。我们在具有挑战性的 BDD100K 数据集和 SODA10M 数据集上对其进行了测试,结果表明 DF-YOLO 具有出色的泛化能力。
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来源期刊
Journal of Real-Time Image Processing
Journal of Real-Time Image Processing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
6.80
自引率
6.70%
发文量
68
审稿时长
6 months
期刊介绍: Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed. Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application. It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system. The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.
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