Because the sensing band of the infrared detector is different from the visible light, it does not depend on the reflection and propagation of atmospheric light, but depends on the radiation intensity emitted by the object itself in the environment, so it often has better target detection effect than the visible light under the conditions of low visibility such as haze and night. Aiming at the problems of low accuracy and poor practicality of target detection in infrared scene, an infrared target detection method based on attention mechanism is proposed. First, a lightweight network structure is designed; second, attention mechanism is used to improve the ability of network feature extraction; then, the iterative feature pyramid structure is improved to improve the detection ability of targets with different scales; finally, complete intersection over union (CIoU) loss function and gradient equilibrium mechanism (GHM) loss function are introduced in the training process to improve the imbalance of positive and negative samples. Compared with other algorithms, the experimental results show that the detection accuracy and speed of the proposed algorithm are significantly improved.