This study investigates the enhancement of the YOLOv5 model for price tag detection in retail environments, aiming to improve both accuracy and robustness. The research utilizes the "Price Tag Detection" dataset from SOVAR, which contains 1,073 annotated images covering four classes: price tags, labels, prices, and products and is split into training, validation, and test sets with extensive preprocessing and augmentation such as resizing, rotation, color adjustments, blur, noise, and bounding box transformations. Several modifications to the YOLOv5 architecture were proposed, including advanced image augmentation techniques to simulate real-world variations in lighting and noise, enhanced anchor box optimization through K-means clustering on the dataset annotations to better fit typical price tag shapes, and the integration of the Convolutional Block Attention Module (CBAM) to enable the model to selectively focus on relevant spatial and channel-wise features. The combined application of these enhancements resulted in a substantial improvement, with the model achieving a mean Average Precision (mAP) of 96.8% at IoU 0.5 compared to the baseline YOLOv5's 92.5%. The attention mechanism and optimized anchor boxes notably improved detection of small, partially occluded, and diverse price tags, highlighting the effectiveness of combining data-driven augmentation, architectural tuning, and attention mechanisms to address the challenges posed by cluttered and dynamic retail scenes.