# Traffic Scene Small Target Detection Method Based on YOLOv8n-SPTS Model for Autonomous Driving

arXiv: https://arxiv.org/abs/2512.09296 · HTML: https://mlsanity.com/paper/2512.09296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [What is YOLOv6? A Deep Insight into the Object Detection Model](https://mlsanity.com/paper/2412.13006/) (2024)

## Abstract (arXiv)

This paper focuses on the key issue in autonomous driving: small target recognition in dynamic perception. Existing algorithms suffer from poor detection performance due to missing small target information, scale imbalance, and occlusion. We propose an improved YOLOv8n-SPTS model, which enhances the detection accuracy of small traffic targets through three key innovations: First, optimizing the feature extraction module. In the Backbone Bottleneck structure of YOLOv8n, 4 traditional convolution modules are replaced with Space-to-Depth Convolution (SPD-Conv) modules. This module retains fine-grained information through space-to-depth conversion, reduces information loss, and enhances the ability to capture features of low-resolution small targets. Second, enhancing feature fusion capability. The Spatial Pyramid Pooling - Fast Cross Stage Partial Connection (SPPFCSPC) module is introduced to replace the original SPPF module, integrating the multi-scale feature extraction from Spatial Pyramid Pooling (SPP) and the feature fusion mechanism of Cross Stage Partial Connection (CSP), thereby improving the model's contextual understanding of complex scenes and multi-scale feature expression ability. Third, designing a dedicated detection structure for small targets. A Triple-Stage Feature Pyramid (TSFP) structure is proposed, which adds a 160*160 small target detection head to the original detection heads to fully utilize high-resolution features in shallow layers; meanwhile, redundant large target detection heads are removed to balance computational efficiency. Comparative experiments on the VisDrone2019-DET dataset show that YOLOv8n-SPTS model ranks first in precision (61.9%), recall (48.3%), mAP@0.5 (52.6%), and mAP@0.5:0.95 (32.6%). Visualization results verify that the miss rate of small targets such as pedestrians and bicycles in occluded and dense scenes is significantly reduced.