Infrared small target detection technology holds important application value across key fields, such as autonomous navigation and security monitoring. This technology specializes in identifying small targets that are challenging to detect with the naked eyes, especially in low-light or obstructed environments. This functionality is of utmost importance for detecting potential threats and enhancing remote sensing capabilities. However, accurately detecting small infrared targets in infrared images presents substantial challenges due to their minimal pixel coverage and lack of shape and texture details. To address these challenges, we propose a deep learning model that integrates a multi-layer convolutional fusion module and a multi-receptive field fusion module. The proposed model aims to effectively represent small targets by extracting features at multiple levels and fusing features from different receptive fields. The model is tested using infrared images captured in a laboratory setting. The experimental results demonstrat that the proposed model performed well across multiple evaluation indicators, achieving a pixel-level intersection-over-union ratio of 0.814 and a sample-level intersection-over-union ratio of 0.845. These results confirm the high accuracy and reliability of the model for small object detection tasks. Furthermore, ablation experiments are conducted to evaluate the influence of different modules on model performance. These experiments confirm that both the multilayer convolutional fusion and multireceptive field fusion modules play crucial roles in improving model performance.