  YOLOv8 plays a crucial role in the realm of autonomous driving, owing to its
high-speed target detection, precise identification and positioning, and
versatile compatibility across multiple platforms. By processing video streams
or images in real-time, YOLOv8 rapidly and accurately identifies obstacles such
as vehicles and pedestrians on roadways, offering essential visual data for
autonomous driving systems. Moreover, YOLOv8 supports various tasks including
instance segmentation, image classification, and attitude estimation, thereby
providing comprehensive visual perception for autonomous driving, ultimately
enhancing driving safety and efficiency. Recognizing the significance of object
detection in autonomous driving scenarios and the challenges faced by existing
methods, this paper proposes a holistic approach to enhance the YOLOv8 model.
The study introduces two pivotal modifications: the C2f_RFAConv module and the
Triplet Attention mechanism. Firstly, the proposed modifications are elaborated
upon in the methodological section. The C2f_RFAConv module replaces the
original module to enhance feature extraction efficiency, while the Triplet
Attention mechanism enhances feature focus. Subsequently, the experimental
procedure delineates the training and evaluation process, encompassing training
the original YOLOv8, integrating modified modules, and assessing performance
improvements using metrics and PR curves. The results demonstrate the efficacy
of the modifications, with the improved YOLOv8 model exhibiting significant
performance enhancements, including increased MAP values and improvements in PR
curves. Lastly, the analysis section elucidates the results and attributes the
performance improvements to the introduced modules. C2f_RFAConv enhances
feature extraction efficiency, while Triplet Attention improves feature focus
for enhanced target detection.
