Aiming at the problem that existing object detection algorithms have low accuracy in full-size object detection, this paper proposes an improved full-size object detection algorithm based on the YOLOv3 model. In the method, a new adaptive recursive FPN network architecture is designed, and a recursive pyramid model based on channel attention is proposed to improve the feature extraction ability of YOLOv3 and the detection ability of objects at different scales. At the same time, loss function transformation is introduced in the training process to solve the problem of dynamic parameters that is not being optimized in the training process. Compared with other mainstream object detection algorithms, the accuracy of small-size objects, large-size objects and multi-size objects with complex backgrounds respectively improved by 5. 6%, 2. 6%, and 1. 6%. Experimental results show that the detection accuracy of the proposed method is significantly improved.