Aiming at the target detection algorithm based on Faster region-based convolutional neural network, we propose an adaptive candidate-region suggestion network. During training, the number of candidate regions is adjusted according to the current loss feedback to ensure that the candidate regions change dynamically in a certain range for cost savings. The number of candidate regions with the best performance is recorded. The recorded candidate regions are tested during testing. An adaptive confidence threshold selection algorithm is proposed to solve the time cost problem and the reduced accuracy of a small target detection caused by artificial confidence threshold selection when Softmax function is used for classifying candidate regions. Experimental results show that compared with the traditional algorithm, the detection speed of the algorithm improves by 25% and the average detection accuracy improves by 1.9 percentage points.