Infrared imaging is a valuable technology for gas leakage detection due to its high sensitivity, long detection range, and high efficiency. Conventional target detection methods depend on manually extracting image features, which often leads to limited accuracy, low adaptability, and slow detection speeds. Deep learning technology offers a potential solution to these challenges; however, the increasing depth of neural networks imposes significant computational demands, posing challenges to real-time detection. This paper presents a compact and energyefficient gas detection system, implemented with a ZYNQ platform and an infrared camera. We propose a ZYNQ-based convolution accelerator to enhance gas plume detection from images captured by the infrared camera. Operating at a clock frequency of 130 MHz, the accelerator is capable of reaching a peak performance of 37.44 Gop/s, with power consumption of only 4.12 W. The system achieves a processing speed of 0.235 seconds per image, enabling real-time gas leakage detection.