EfficientFCOS: An Efficient One-stage Object Detection Model based on FCOS

Object detection is playing an important role in computer vision. With the rapid development of deep learning, object detection algorithms based on convolutional neural networks have been successful. However, deep learning requires a large amount of data to train, it is crucial to improve model efficiency with the same required hardware resources. In this paper, we propose an efficient one-stage object detection model, EfficientFCOS based on pixel-level prediction, which can improve the accuracy and efficiency of existing one-stage object detection network model. In particular, EfficientFCOS first uses EfficientNet to extract the input image features, which has fewer parameters and better performance than ResNet. Next, it employs the scaling approaches to uniformly scale number of channels of the model’s backbone network, feature fusion network and shared head network according to the resolution of the image. Furthermore, it adopts the weighted bidirectional feature pyramid network to achieve fast and multi-scale feature fusion. Meanwhile, it integrates the geometric factors (center point distance, overlap rate and scale) to the regression loss of target prediction, such that the regression of the target prediction box becomes more stable. Our experimental results indicate that EfficientFCOS is more efficient than the existing FCOS. On the Pascal Voc data set, EfficientFCOS achieves 82.1 for the mAP value, which increases 2.8%. It has 15M network parameters, which is 4.3x smaller than FCOS. Meanwhile, it improves the GPU LAT and CPU LAT by 12.2% and 20.0% respectively.