Real-time traffic object detection is essential for autonomous driving and intelligent transportation systems, where detection accuracy must be achieved under strict computational constraints. This study proposes a lightweight optimization framework for YOLOv5s-based traffic object detection and evaluates its effectiveness on a subset of the BDD100K benchmark. The framework incorporates three complementary optimization strategies. First, the original CIoU regression loss is replaced with SIoU to introduce directional information into bounding-box optimization and improve localization efficiency. Second, a Convolutional Block Attention Module (CBAM) is incorporated between the SPPF module and the neck to strengthen the representation of informative channel and spatial features in complex traffic scenes. Third, the genetic-algorithm-based hyperparameter evolution mechanism of YOLOv5 is employed to automatically adapt key training and augmentation parameters to the target traffic dataset. Experimental results show that the proposed method increases mAP@0.5 from 46.7% to 54.7% and mAP@0.5:0.95 from 23.2% to 27.1%, corresponding to absolute improvements of 8.0 and 3.9 percentage points, respectively. Class-level evaluation indicates particularly notable gains for traffic signs (+12.1 percentage points), traffic lights (+10.8 percentage points), and riders (+10.3 percentage points). Compared with larger YOLOv5 variants and YOLOv8s evaluated under the same protocol, the proposed detector provides competitive detection performance while retaining the compact characteristics of the YOLOv5s framework. The results demonstrate that coordinated optimization of the regression objective, feature representation, and training configuration can effectively improve lightweight traffic object detection without relying primarily on major increases in model complexity.