Search Results (4)

Against the background of the sky, imaging and perception of aircraft are crucial for various vision applications. Thanks to the ever-evolving nature of the convolutional neural network (CNN), it has become easier to distinguish and recognize different types of aircraft. Nevertheless, accurate classification for sub-categories of aircraft still poses great challenges. On one hand, fine-grained recognition focuses on exploring and studying such problems. On the other hand, aircraft under different sub-categories and granularities put forward higher requirements for feature representation to classify, which led us to rethink the in-depth application of features. We noticed that information in the swin-transformer effectively represents the features in neural network layers, fully showcasing encoding and indexing for information. Through further research based on this, we proposed a better understanding of encoding and reuse for features, and innovatively performed feature coding graphically for classification. In this paper, our approach shows the effects on aircraft feature representation and classification, manifested from the flexible recognition effect at different aircraft category granularities, and outperforms other famous fine-grained classification models on this vision task. Not only did the approach we proposed demonstrate adaptability to aircraft at different classification granularities, but it also revealed the mechanisms and characteristics of feature encoding under different sample space partitions for classification. The relationship between the oriented representation of aircraft features and various classification granularities, which is manifested through different classification criteria, shows that feature coding and graph construction via the transformer opens a new door for specific defined classification tasks where objects are divided under various partition criteria, and provides another perspective on calculation and feature extraction in fine-grained classification. Full article

In order to realize the detection and recognition of specific types of an aircraft in remote sensing images, this paper proposes an algorithm called Fine-grained S²ANet (FS²ANet) based on the improved Single-shot Alignment Network (S²ANet) for remote sensing aircraft object detection and fine-grained recognition. Firstly, to address the imbalanced number of instances of various aircrafts in the dataset, we perform data augmentation on some remote sensing images using flip and color space transformation methods. Secondly, this paper selects ResNet101 as the backbone, combines space-to-depth (SPD) to improve the FPN structure, constructs the FPN-SPD module, and builds the aircraft fine feature focusing module (AF³M) in the detection head of the network, which reduces the loss of fine-grained information in the process of feature extraction, enhances the extraction capability of the network for fine aircraft features, and improves the detection accuracy of remote sensing micro aircraft objects. Finally, we use the SkewIoU based on Kalman filtering (KFIoU) as the algorithm’s regression loss function, improving the algorithm’s convergence speed and the object boxes’ regression accuracy. The experimental results of the detection and fine-grained recognition of 11 types of remote sensing aircraft objects such as Boeing 737, A321, and C919 using the FS²ANet algorithm show that the mAP_0.5 of FS²ANet is 46.82%, which is 3.87% higher than S²ANet, and it can apply to the field of remote sensing aircraft object detection and fine-grained recognition. Full article

Fine-grained aircraft type recognition in remote sensing images, aiming to distinguish different types of the same parent category aircraft, is quite a significant task. In recent decades, with the development of deep learning, the solution scheme for this problem has shifted from handcrafted feature design to model architecture design. Although a great progress has been achieved, this paradigm generally needs strong expert knowledge and rich expert experience. It is still an extremely laborious work and the automation level is relatively low. In this paper, inspired by Neural Architecture Search (NAS), we explore a novel differentiable automatic architecture design framework for fine-grained aircraft type recognition in remote sensing images. In our framework, the search process is divided into several phases. Network architecture deepens at each phase while the number of candidate functions gradually decreases. To achieve it, we adopt different pruning strategies. Then, the network architecture is determined through a potentiality judgment after an architecture heating process. This approach can not only search deeper network, but also reduce the computational complexity, especially for relatively large size of remote sensing images. When all differentiable search phases are finished, the searched model called Fine-Grained Aircraft Type Recognition Net (FGATR-Net) is obtained. Compared with previous NAS, ours are more suitable for relatively large and complex remote sensing images. Experiments on Multitype Aircraft Remote Sensing Images (MTARSI) and Aircraft17 validate that FGATR-Net possesses a strong capability of feature extraction and feature representation. Besides, it is also compact enough, i.e., parameter quantity is relatively small. This powerfully indicates the feasibility and effectiveness of the proposed automatic network architecture design method. Full article

Aircraft recognition in remote sensing images has long been a meaningful topic. Most related methods treat entire images as a whole and do not concentrate on the features of parts. In fact, a variety of aircraft types have small interclass variance, and the main evidence for classifying subcategories is related to some discriminative object parts. In this paper, we introduce the idea of fine-grained visual classification (FGVC) and attempt to make full use of the features from discriminative object parts. First, multiple class activation mapping (MultiCAM) is proposed to extract the discriminative parts of aircrafts of different categories. Second, we present a mask filter (MF) strategy to enhance the discriminative object parts and filter the interference of the background from original images. Third, a selective connected feature fusion method is proposed to fuse the features extracted from both networks, focusing on the original images and the results of MF, respectively. Compared with the single prediction category in class activation mapping (CAM), MultiCAM makes full use of the predictions of all categories to overcome the wrong discriminative parts produced by a wrong single prediction category. Additionally, the designed MF preserves the object scale information and helps the network to concentrate on the object itself rather than the interfering background. Experiments on a challenging dataset prove that our method can achieve state-of-the-art performance. Full article