DAF-Net: dense attention feature pyramid network for multiscale object detection

In recent years, object detection has become one of the most prominent components in computer vision. State-of-the-art object detectors now employ convolutional neural networks (CNNs) techniques alongside other deep neural network techniques to improve detection performance and accuracy. Most of the recent object detectors employ feature pyramid network (FPN) and their variants while others use combinations of attention mechanisms to achieve better performance. The open question is object detectors inconsistency between the lower layer features, their resolution receptive field and semantic information with the upper layers features in detecting objects. Although some researchers have attempted to address this issue, we exploit ideas surrounding the field and proposed a more prominent architecture called dense attention feature pyramid network (DAF-Net) for multiscale object detection. DAF-Net consists of two attention models, the spatial attention model and channel attention model. Different from other attention models, we proposed lightweight attention models which are fully data-driven then implemented a dense connected attention FPN to reduce the model’s complexity and resolve the learning of redundant feature maps. First, we developed the two attention models then used only the spatial attention model in the backbone of our network, and finally used both attention models to filter and maintain a steady flow of semantic information from lower layers to improve the model’s accuracy and efficiency. Experimental results on underwater images from the National Natural Science Foundation of China (NSFC) (Underwater Image Dataset, National Natural Science Foundation of China (NSFC). Online, retrieved from http://​www.​cnurpc.​org/​index.​html), MS COCO dataset, and PASCAL VOC dataset indicate higher accuracy and better detection results using the proposed model compared to the benchmark model YOLOX-Darknet53 (Ge in Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430). Our model achieved 70.2mAP, 48.9 mAP, and 83.9 mAP on (NSFC), MS COCO, and PASCAL VOC datasets, respectively, compared with benchmark model 68.9mAP on (NSFC), 47.7mAP on MS COCO, and 82.4mAP on PASCAL VOC.