Combining Lightweight and Multiscale Fusion for Traffic Sign Detection Algorithm
Traffic sign detection is critical for autonomous driving is of essence,as timely and accurate identification of these signs enhances driving safety and reduces the risk of traffic accidents.However,in complex environments,small-sized and obstructed traffic signs are often missed or incorrectly identified.To address this problem,a novel traffic-sign detection network architecture,Multiple You Only Look Once(M-YOLO),is proposed based on the YOLOv8 framework.M-YOLO integrates lightweight design and multiscale fusion to achieve precise detection tasks.A variant,Multiple YOLO small(M-YOLOs),is developed by adjusting network depth,and a lighter version,Multiple YOLO nano(M-YOLOn),is created to meet detection requirements in various environments.To enhance the detection of small traffic signs and mitigate feature loss,the network's feature learning capability is improved by incorporating a small target detection layer.This layer preserves more feature information,and a multiscale feature pyramid fusion network,Multiple Path Aggregation Network(MPANet),is proposed to reduce the dimensionality of shallow feature maps while using skip connections to fuse additional image feature information.Furthermore,a Bi-level Routing and Spatial Attention(BRSA)module is introduced to combine sparse and spatial attention,extracting both global and local positional information while reducing interference from complex backgrounds.To optimize the model,two lightweight and efficient modules,BBot and C2fGhost,are developed to reduce parameters and enhance computational speed.Experimental results demonstrated that M-YOLO reduced the number of parameters by approximately one-third compared to YOLOv8.On the TT100K and German Traffic Sign Detection Benchmark(GTSDB)datasets,the detection accuracy of M-YOLOs improved by 9.7 and 2.1 percentage points,respectively,and M-YOLOn achieved improvements of 14.5 and 2.6 percentage points.This lightweight approach led to enhanced detection performance,effectively addressing the issue of information loss during feature extraction from flat feature maps and significantly reducing unnecessary computations in the model.The effectiveness of the M-YOLO architecture is validated on a dataset collected from rea-world scenarios,demonstrating its practical value in traffic sign detection tasks.
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