A novel detection framework is proposed based on a multiscale convolutional neural network (MSCNN) to overcome low precision and insufficient generalization ability associated with existing object detection methods for multiscale objects with complex scenes. First, an essence feature pyramid network is constructed to enhance the extraction ability of multiscale features. Then, the focal classification loss is introduced as classification loss function to enhance the learning capability of the MSCNN over complex samples. The proposed method achieves a mean average precision(mAP) of 0.960 over the challenging NWPU VHR-10 dataset. In comparison with the RetinaNet detection method, the mAP of the proposed MSCNN on small- and medium-scale objects increases by 1.5% and 1.9%, respectively. The proposed method is found to be accurate and robust for multiscale objects.