Object detection is a fundamental challenge encountered in understanding and analyzing remote sensing images. Many current detection methods struggle to identify objects in remote sensing images due to poor feature saliency, diverse directions, and unique viewing angles of small remote sensing targets. As most remote sensing images are captured through overhead photography, the structural contour features of objects in these images remain relatively stable, such as the cross-shaped structure of the aircraft and the rectangular structure of the vehicle. Therefore, we utilize the physical simulation images as prior knowledge to supplement the invariant structural features of the target, extract the target’s key geometric features through the dynamic matching method, and fuse it with the features extracted by the neural network to detect the remote sensing small target more effectively in this article. We propose a template matching method based on physical simulation images (TMSI) and add it to the modified Darknet-53 (named TMSI-Net) for small target detection. Based on this, we perform specific transformations on existing templates in terms of target scale and direction to achieve better adaptation to the corresponding goals and propose DTMSI-Net with a dynamic template library (Dynamic TMSI-Net). Experiments on several datasets demonstrate that the DTMSI-Net exhibits higher detection accuracy and more stable performance compared with the state-of-the-art methods, 4% and 2.7% higher than the second-ranked model on the VEDAI and NWPU datasets, respectively.

Existing X-ray prohibited item detection methods primarily focus on boosting the detection performance of uniformly distributed items. However, in the real-world scenarios, various prohibited items exhibit the long-tailed distribution, thus posing the huge challenge to the detection task. To support this study, we introduce LTXRay, a dedicated X-ray benchmark that better assesses long-tailed prohibited item detection. LTXRay consists of 18,718 images from 12 common classes with an imbalance factor of 280.35. Meanwhile, we propose a novel Memory-Guided Learning Network(MGLNet) to develop baseline methods on LTXRay, which enhance the within-class diversity for the tail classes and consequentially improves long-tailed object detection. Specifically, we first introduce a frequency-based feature refinement module to extract discriminative contextual representations, then store the various instance features in the memory bank and dynamically generate the sample according to the historical features. Extensive experiments have been performed on the LTXRay to demonstrate the effectiveness of the proposed method. The experimental results indicate that the proposed method can consistently improve the performance of baseline methods.

Automatic crack detection is challenging, owing to the complex and thin topologies, diversity, and background noises of cracks. Inspired by the wavelet theory, we present an instance normalization wavelet (INW) layer and embed the layer into the deep model for segmentation. The proposed layer employs prior knowledge in the wavelets to capture the crack features and filter the high-frequency noises simultaneously, accelerating the convergence of model training. Furthermore, instance normalization in our layer is utilized to mitigate the feature differences, boosting the generalization capability. In addition, a fusion layer is added to merge the information across the different layers. The comparison experiments and ablation studies demonstrate that the INW layer steadily enhances recognition and convergence performance on the DeepCrack dataset and CRACK500 dataset.

Multi-scale object detection in remote sensing imagery poses significant challenges, including substantial variations in object size, diverse orientations, and interference from complex backgrounds. To address these issues, we propose SADFF-Net, a novel detection framework that incorporates a Multi-scale Contextual Attention Fusion (MCAF) module to enhance information exchange between feature layers and suppress irrelevant feature interference. In addition, SADFF-Net employs an Adaptive Spatial Feature Fusion (ASFF) module to improve semantic consistency across feature layers by assigning spatial weights at multiple scales. To enhance adaptability to scale variations, the regression head integrates a deformable convolution. In contrast, the classification head utilizes depth-wise separable convolutions to significantly reduce computational complexity without compromising detection accuracy. Extensive experiments on the DOTAv1 and DIOR_R datasets demonstrate that SADFF-Net outperforms current state-of-the-art methods in multi-scale object detection.

Compared with traditional artificial neural networks (ANNs), spiking neural networks (SNNs) have lower computational complexity, lower energy consumption, and faster inference speed, making them more promising for practical deployment on edge devices. However, in SNNs, since the output spikes of neurons are discrete, the model may face information loss, especially when the membrane potential is quantized into binary spikes, where quantization errors can lead to model precision loss and information loss. To address these challenges, this study proposes a spiking neural network based on hierarchical residual quantization and temporal error compensation (HRQ-TEC-SNN) and used for remote sensing object detection tasks. Through hierarchical residual quantization and temporal error compensation design, higher resolution quantization of membrane potential can be performed and the quantization threshold of membrane potential can be dynamically adjusted to compensate for errors introduced during the quantization process, allowing for fine-tuning at each time step and reducing information loss caused by coarse quantization. In terms of network structure, by introducing depthwise separable convolution modules, channel attention and spatial attention mechanisms, and integrating fast spatial pyramid pooling based on pulse neural networks, the model’s detection accuracy has been further improved while reducing the number of model parameters and computational costs. Experimental results show that the HRQ-TEC-SNN achieves significant advantages in both accuracy and energy consumption on the DOTAv1.0, DOTAv1.5 and DIOR datasets.