In the past few decades, target detection from remote sensing images gained from aircraft or satellites has become one of the hottest topics. However, the existing algorithms are still limited by the detection of small remote sensing targets. Benefiting from the great development of computing power, deep learning has also made great breakthroughs. Due to a large number of small targets and complexity of background, the task of remote sensing target detection is still a challenge. In this work, we establish a series of feature enhancement modules for the network based on YOLO (You Only Look Once) -V3 to improve the performance of feature extraction. Therefore, we term our proposed network as FE-YOLO. In addition, to realize fast detection, the original Darknet-53 was simplified. Experimental results on remote sensing datasets show that our proposed FE-YOLO performs better than other state-of-the-art target detection models.

High-altitude remote sensing target detection has problems related to its low precision and low detection rate. In order to enhance the performance of detecting remote sensing targets, a new YOLO (You Only Look Once)-V3-based algorithm was proposed. In our improved YOLO-V3, we introduced the concept of multi-receptive fields to enhance the performance of feature extraction. Therefore, the proposed model was termed Multi-Receptive Fields Fusion YOLO (MRFF-YOLO). In addition, to address the flaws of YOLO-V3 in detecting small targets, we increased the detection layers from three to four. Moreover, in order to avoid gradient fading, the structure of improved DenseNet was chosen in the detection layers. We compared our approach (MRFF-YOLO) with YOLO-V3 and other state-of-the-art target detection algorithms on an Remote Sensing Object Detection (RSOD) dataset and a dataset of Object Detection in Aerial Images (UCS-AOD). With a series of improvements, the mAP (mean average precision) of MRFF-YOLO increased from 77.10% to 88.33% in the RSOD dataset and increased from 75.67% to 90.76% in the UCS-AOD dataset. The leaking detection rates are also greatly reduced, especially for small targets. The experimental results showed that our approach achieved better performance than traditional YOLO-V3 and other state-of-the-art models for remote sensing target detection.

Over the past few decades, advances in satellite and aerial imaging technology have made it possible to acquire high-quality remote sensing images. As one of the most popular research directions of computer vision, remote sensing object detection is widely researched due to the wide application in military and civil fields. The algorithms based on convolutional neural network have made great achievements in the field of object detection. However, plenty of small and densely distributed remote sensing objects against complex background pose some challenges to object detection. In this work, an efficient anchor-free based remote sensing object detector based on YOLO (You Only Look Once) is constructed. Firstly, the backbone network is simplified for the high efficiency of detection. In order to extract the features of densely distributed objects effectively, the detection scales are adjusted based on the backbone network. Secondly, aiming at the shortcomings of CBAM, the improved CJAM (Coordinate Joint Attention Mechanism) is proposed to deal with object detection under complex background. In addition, feature enhancement modules DPFE (Dual Path Feature Enhancement) and IRFE (Inception-ResNet-Feature Enhancement) as well as PRes2Net (Parallel Res2Net) are proposed. We combine CJAM with the above modules to create DC-CSP_n, CSP-CJAM-IRFE, and CJAM-PRes2Net for better feature extraction. Thirdly, a lightweight auxiliary network is constructed to integrate the low-level and intermediate information extracted from remote sensing images into the high-level semantic information of the backbone network. The auxiliary network allows the detector to locate the target efficiently. Fourthly, Swin Transformer is introduced into the ‘Neck’ part of the network so that the network can effectively grasp the global information. The mAP on DOTA1.5 and VEDAI datasets, which both contain a large number of small objects, reached 77.07% and 63.83%, respectively. Compared with advanced algorithms such as YOLO V4, YOLO V5s, YOLO V5l, and YOLO V7, our approach achieves the highest mAP.