YOLOv8-MPEB small target detection algorithm based on UAV images

Wenyuan Xu; Chuang Cui; Yongcheng Ji; Xiang Li; Shuai Li

doi:10.1016/j.heliyon.2024.e29501

YOLOv8-MPEB small target detection algorithm based on UAV images

Heliyon. 2024 Apr 15;10(8):e29501. doi: 10.1016/j.heliyon.2024.e29501. eCollection 2024 Apr 30.

Authors

Wenyuan Xu¹, Chuang Cui¹, Yongcheng Ji¹, Xiang Li¹, Shuai Li¹

Affiliation

¹ School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China.

Abstract

Target detection in Unmanned Aerial Vehicle (UAV) aerial images has gained significance within UAV application scenarios. However, UAV aerial images present challenges, including large-scale changes, small target sizes, complex scenes, and variable external factors, resulting in missed or false detections. This study proposes an algorithm for small target detection in UAV images based on an enhanced YOLOv8 model termed YOLOv8-MPEB. Firstly, the Cross Stage Partial Darknet53 (CSPDarknet53) backbone network is substituted with the lightweight MobileNetV3 backbone network, consequently reducing model parameters and computational complexity, while also enhancing inference speed. Secondly, a dedicated small target detection layer is intricately designed to optimize feature extraction for multi-scale targets. Thirdly, the integration of the Efficient Multi-Scale Attention (EMA) mechanism within the Convolution to Feature (C2f) module aims to enhance the extraction of vital features and suppress superfluous ones. Lastly, the utilization of a bidirectional feature pyramid network (BiFPN) in the Neck segment serves to ameliorate detection errors stemming from scale variations and complex scenes, thereby augmenting model generalization. The study provides a thorough examination by conducting ablation experiments and comparing the results with alternative algorithms to substantiate the enhanced effectiveness of the proposed algorithm, with a particular focus on detection performance. The experimental outcomes illustrate that with a parameter count of 7.39 M and a model size of 14.5 MB, the algorithm attains a mean Average Precision (mAP) of 91.9 % on the custom-made helmet and reflective clothing dataset. In comparison to standard YOLOv8 models, this algorithm elevates average accuracy by 2.2 percentage points, reduces model parameters by 34 %, and diminishes model size by 32 %. It outperforms other prevalent detection algorithms in terms of accuracy and speed.

Keywords: Attention mechanism; BiFPN; MobileNetV3; Small target detection; YOLOv8.