# Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
## 5 Conclusions  
This paper presents an improved algorithm for under-vehicle security inspection based on YOLOv8n, named PSP-YOLO. The original YOLOv8n model faces issues with missing detections and low accuracy when identifying small dangerous objects. To address these challenges, the PSP-YOLO algorithm introduces a P2 tiny object-detection head and the SPD-Conv module, effectively combining these two innovations to significantly enhance the performance of the original model. Additionally, the integration of the PConv module reduces the number of parameters and computational load, leading to improvements in both detection accuracy and speed. Compared to the YOLOv8n algorithm, PSP-YOLO achieves increases of 3.9% in [EMAIL_ADDRESS] and 3.6% in [EMAIL_ADDRESS]-0.95 on a dangerous objects dataset, with a parameter count of 2.53 M and a detection speed of 104.6 frames per second (FPS). These improvements meet the accuracy and speed requirements for under-vehicle inspection scenarios. Future research will focus on model lightweight and optimization to enhance computational efficiency and reduce reliance on hardware resources, especially for applications on resource-constrained edge devices. In this study, we significantly improved the small-object detection capability and overall detection accuracy of the YOLOv8n model by introducing several innovative modules. However, these improvements also resulted in an increased computational load, posing challenges for deployment on embedded or low-power devices. To address this issue, future research will concentrate on the following directions:  
1. Model compression and knowledge distillation: by using model compression to remove redundant parameters and reduce the model size, combined with knowledge distillation to maintain accuracy, computational efficiency can be improved. 2. Exploration of efficient network architectures: research will explore more efficient network architectures, such as dynamic convolutions and attention mechanisms, to improve model performance in complex scenarios, particularly enhancing the accuracy of small-object detection. 3. Collaborative optimization with hardware architectures: research will focus on how to design efficient models that take hardware characteristics into account, improving performance on edge devices and addressing resource limitations. Figure 9: Algorithm improvement contrast graph. Through these optimizations, the improved model is expected to achieve a better balance between accuracy and efficiency, making it more suitable for small-object detection tasks in edge device scenarios and offering broader value for practical applications such as under-vehicle safety detection. Conceptualization, D.Z. and Y.C. ; methodology, Y.C. ; software, Y.C. ; validation, Y.C. and S.M. ; formal analysis, Y.C. ; investigation, D.Z. ; resources, D.Z. ; data curation, Y.C. ; writing--original draft preparation, Y.C. ; writing--review and editing, Y.C. ; visualization, Y.C. ; supervision, S.M. ; project administration, D.Z. ; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Hubei Province (High-end Equipment Manufacturing Industry) High-value Patent Cultivation Center Project (2024-211). Not applicable. The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study. The authors declare no conflicts of interest.