Robust detection of infrared small targets under complex backgrounds is of great significance for infrared search and tracking applications. However, the inherent problem of limited prior features for infrared small targets has always made its detection task a challenging research topic. In order to solve the problem, we propose a novel infrared small target detection (IRSTD) method based on monogenic signal decomposition and feature expansion, which can effectively enrich and extract the potential features of the target. First, a series of local information of the original image is obtained through the monogenic signal constructed by the Riesz transform. Then, various features of the small target are extracted from different local signals, including the direction feature, edge feature, and local saliency feature. Finally, the fusion of target features is completed through signal reconstruction, thereby achieving target detection. This method not only pays attention to the local salient characteristic of the target but also supplements the consideration of other characteristics of the target, providing a new idea for small target detection. The experimental results on real infrared images show that the proposed method framework is reasonable and effective, and possesses better detection performance and good generalization compared to other state-of-the-art methods.

Infrared Small Target Detection Based on Monogenic Signal Decomposition

A deep Retinex network for underwater low-light image enhancement

Infrared (IR) small target detection is a critical technology in both military and civil applications. Nowadays, the optimization-based methods are widely concerned for their good performance under complex backgrounds. However, these optimization models are constructed simply, without sufficient information to fully characterize the features of an image. Moreover, the true target is unprotected in the target weight, and appropriate structural constraints on background are also deficient. Consequently, an IR small target detection algorithm based on target-and-background-weighted two-layer nested model is proposed in this letter. Initially, a two-layer nested model is designed to characterize the global and the local structure of an IR image simultaneously. Then, the smallest eigenvalue of structure tensor (SEoST) is introduced to reweight the overlapping edge information (OEI) map as a target weight, in order to reduce the false alarm rate without missed detection. Furthermore, a weight matrix of background is elaborated as a structural constraint, in order to get a more accurate background estimation result. Finally, the alternating direction multiplier method (ADMM) with a soft-threshold solver is designed to solve the proposed optimization model. Experiments are carried out to evaluate the proposed method by comparing with eight baseline methods, and the results show that the proposed method achieves superior detection performance under complex scenes.

Infrared Small Target Detection Based on Target-and-Background-Weighted Two Layer Nested Model

In this paper, we are interested in how computers can be used to better serve us humans, such as helping humans control their nutrient intake, with higher level shortcuts. Specifically, the neural network model was used to help humans identify and analyze the content and proportion of nutrients in daily food intake, so as to help humans autonomously choose and reasonably match diets. In this study, we formed the program we wanted to obtain by establishing four modules, in which the imagination module sampled the environment, then relied on the encoder to extract the implicit features of the image, and finally relied on the decoder to obtain the required feature vector from the implicit features, and converted it into the battalion formation table information through the semantic output module. Finally, the model achieved extremely high accuracy on recipe1M+ and food2K datasets.

DelicacyNet for nutritional evaluation of recipes

Road target detection is essential for enhancing vehicle safety, increasing operational efficiency, and optimizing user experience. It also forms a crucial part of autonomous driving and intelligent monitoring systems. However, current technologies face significant limitations in multi-level feature fusion and the accurate identification of key targets in complex data environments. To address these challenges, this paper proposes an innovative algorithmic model called BiFPN GAM SimC2f-YOLO (BGS-YOLO), aimed at improving detection performance. Initially, this paper employs the Bidirectional Feature Pyramid Network (BiFPN) to effectively integrate multi-level features. This integration overcomes the limitations in feature extraction and recognition found in existing target detection algorithms. Following this, this paper introduces the Global Attention Module (GAM), which markedly improves the efficiency and accuracy of extracting key target information in complex data environments. Additionally, this paper innovatively designs the SimAM-C2f (SimC2f) network, further advancing feature expressiveness and fusion efficiency. Experiments on the public COCO dataset demonstrate that the BGS-YOLO model significantly outperforms the existing YOLOv8n model. Notably, it shows a 7.3% increase in mean average precision (mAP) and a 2.4% improvement in accuracy. These results highlight the model’s high precision and swift response in detecting road targets in complex traffic scenarios. Consequently, the BGS-YOLO model has the potential to significantly enhance road safety and contribute to a considerable reduction in traffic accident rates.

Enhancing Intelligent Road Target Monitoring: A Novel BGS YOLO Approach Based on the YOLOv8 Algorithm

Infrared dim and small target detection is a key technology for various detection tasks. However, due to the lack of shape, texture, and other information, it is a challenging task to detect dim and small targets. Recently, since many traditional algorithms ignore the global information of infrared images, they generate some false alarms in complicated environments. To address this problem, in this paper, a coarse-to-fine deep learning-based method was proposed to detect dim and small targets. Firstly, a coarse-to-fine detection framework integrating deep learning and background prediction was applied for detecting targets. The framework contains a coarse detection module and a fine detection module. In the coarse detection stage, Region Proposal Network (RPN) is employed to generate masks in target candidate regions. Then, to further optimize the result, inpainting is utilized to predict the background using the global semantics of images. In this paper, an inpainting algorithm with a mask-aware dynamic filtering module was incorporated into the fine detection stage to estimate the background of the candidate targets. Finally, compared with existing algorithms, the experimental results indicate that the proposed framework has effective detection capability and robustness for complex surroundings. 

Infrared Dim and Small Target Detection Based on Background Prediction

The underwater environment is often poorly illuminated, making the images captured during underwater exploration missions poorly visible. Therefore, artificial light sources are often used to assist in underwater imaging. However, artificial light sources usually change the light conditions to a large extent, resulting in nonuniform illumination. To solve this problem, we propose a pseudo-siamese network that enables the separation of the nonuniform illumination layer and the ideal illumination image. And cascading iterative operation is utilized to achieve enhancement of image details and improve the image quality. To better balance the estimation quality of both, we propose residual reconstruction loss and structure loss as further guidance for network training. In addition, we simulate a nonuniform illumination model to create a dataset containing nonuniform illumination layers and ideally illuminated uniform images to alleviate the problem of insufficient data. The comprehensive experiments show that the algorithm can effectively correct the underwater nonuniform illumination image and recover the details of images better than the existing correction algorithms.

Nonuniform illumination correction for underwater images through a pseudo-siamese network

Infrared small target detection technology has an essential role in various target detection field. Nonetheless, it is a challenging task to detect small targets due to the lack of shape, texture, and other information. Recently, many traditional algorithms (e.g., Top-Hat transformation) detect numerous false alarms focusing on local regions of infrared images, which leads they are not sufficient to fulfill the requirements. To address this problem, in this paper, an infrared detection framework is proposed to detect small targets. The framework is composed of Top-Hat transformation and image inpainting. Foremost, Top-Hat with mean filtering detect suspicious target areas. Then, to further refine the targets, a local background estimation is designed into the framework. Moreover, inpainting with diffusion model is employed to obtain the background by the global semantic. Eventually, experimental results on several infrared sequences indicate the proposed framework has an effective detection capability and robustness to different environments.

An Inpainting Based Infrared Small Target Detection Method

Most target tracking is performed on visible video. This conventional RGB video lacks spectral information, leading to the tracking process being susceptible to interference from background clutter, illumination changes, etc. Hyperspectral images (HSIs) have the advantage of spectral integration, which can interpret the features of a target in both spatial and spectral dimensions. Therefore, HSIs with continuous spectral information have unique advantages in the field of target tracking. In this paper, a correlation filter-based HSIs tracking method is proposed. Feature maps are generated using tensor singular spectrum analysis (tensorS SA) feature and depth features to jointly exploit spatial contextual information and deep semantic information. Then a kernelized correlation filtering framework is used to generate feature responses on HSIs, and the largest region in the final output response is the center of the target in the current frame. Experiments conducted on hyperspectral images show that the proposed method can effectively utilize spatial-spectral information and outperforms the hyperspectral video tracking methods that partially use single features and the traditional color video tracking methods.

Hyperspectral Video Target Tracking Based on TensorSSA and Deep Features

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Papers

Infrared Dim and Small Target Detection Based on Background Prediction

Nonuniform illumination correction for underwater images through a pseudo-siamese network

An Inpainting Based Infrared Small Target Detection Method

Hyperspectral Video Target Tracking Based on TensorSSA and Deep Features