# Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
## 1 Introduction  
Concealment of dangerous objects beneath vehicles poses a significant threat to public safety due to the difficulty in detection and the ease with which such threats can evade law enforcement searches. This issue is particularly critical in contexts such as large-scale events, key government facility entrances, highway toll stations, prison entrances, airport vehicle checkpoints, and parking areas surrounding sensitive locations, where the accuracy and speed of vehicle inspection systems are stringently required [1]. Consequently, developing methods for accurately, rapidly, and conveniently identifying potential risks beneath vehicles is an urgent issue that demands resolution. Under-vehicle detection methods have evolved from initial manual inspections, to the use of handheld detection devices, and now to automated recognition using machine vision cameras. Machine learning-based visual object-detection methods have significantly enhanced efficiency while reducing the burden on human operators, making them pivotal in achieving automatic under-vehicle inspections. Domestic scholars have made significant strides in under-vehicle object detection using machine learning techniques. [PERSON] et al. [2] proposed a detection method for high-risk targets on the vehicle bottom of a security robot. Their approach involved improving the Speeded up Robust Feature (SURF) algorithm to eliminate mismatchedpoints and enhancing the Random Sample Consensus (RANSAC) algorithm for image correction. The final step employed a convolution-based NCC block-matching algorithm to mark the locations of dangerous objects, resulting in improved accuracy and detection speed. However, the method was computationally intensive and exhibited low feature-extraction efficiency. [PERSON] et al. [3] proposed the WTFE-TIN image registration algorithm, designed to locate fault targets under vehicles in weakly textured regions. This algorithm combined Mean-Shift clustering with a matching method based on gray-level differences and normalized correlation coefficients, achieving a matching accuracy of over 80%. Nonetheless, it failed to accurately locate three types of fault targets, and its detection speed was slow, with a high computational cost. [PERSON] et al. [4] introduced a vehicle foreign-object detection algorithm based on an innovative adversarial autoencoder. In this algorithm, an attention mechanism is incorporated into the adversarial autoencoder structure, and feature-matching loss is used to optimize the loss function. While the algorithm was effective in detecting objects under the bogie and on the sides, it suffered from low detection speed and a high rate of false positives and false negatives in bogie scenarios. [PERSON] et al. [5] developed a deep learning-based visual inspection system for front chassis assembly. This system first used a fringe-recognition algorithm based on differential statistics to eliminate the low-quality images, followed by the YOLOv3-Tiny detection algorithm for target identification. Although the system achieved high accuracy, its performance in occluded and low-light scenarios was suboptimal. [PERSON] et al. [6] enhanced detection accuracy by building on the YOLOv5s network, incorporating Mixup data augmentation, replacing the CIOU loss function with SIoU, and integrating ShuffleNet v2 and Ghost convolution modules into the backbone and neck. Despite these improvements, the model remained computationally demanding, and there was still considerable room for improvement in detection speed. To address these challenges, this paper proposes an improved YOLOv8n object-detection algorithm, termed PSP-YOLO. The YOLO [7; 8; 9; 10; 11; 12] series of algorithms was first introduced in 2016, with YOLOv1 achieving real-time object detection through full-image convolution. In 2017, YOLOv2 improved the network structure by introducing batch normalization and higher-resolution inputs, enhancing both precision and speed.