The existing object detection methods for low-light image usually separate image restoration from object detection tasks. In addition, the quality and computing time of image restoration cannot meet the requirements of object detection task. To solve these problems, firstly, this study proposes an efficient image restoration convolutional neural network architecture, which aggregates feature information of multi-level contexts by combining feature maps of different scales, reduces information redundancy of convolutional layers, and improves the real-time performance of image restoration. In addition, a local-global attention block is designed to improve the ability of the recovery network to distinguish between noise and image content by calibrating the local information of each feature map and the relationship between feature channels. Secondly, this study designs a solution for collaborative processing of image restoration and target recognition tasks. The high-level semantic information of target recognition is used to guide the image recovery network learning, so as to highlight the feature information such as the structure and texture of the target, and make the recovery result more suitable for the target recognition task. Experimental results show that this method is superior to the existing methods in image restoration quality, computing time and object detection rate.