Infrared small-target detection plays a crucial role in applications such as public safety monitoring. However, it faces significant challenges due to the loss of target features, which weakens detection performance. To tackle this problem, this study proposes an improved infrared small-target detection model based on YOLOv8n. First, the Dual-Path Fusion Downsampling Convolution (WFDC) module enhances the backbone network’s ability to extract fine-grained features of targets, preventing the loss of image details as the depth of the convolutional neural network increases. Second, the Involution and the Coordinate Attention (CA) mechanisms are integrated into the spatial pyramid pooling module, where self-attention and the Involution modules aggregate contextual semantic information over a broader spatial range, enriching channel information at each scale. Finally, deformable convolutions are incorporated into the backbone of the model, enabling better handling of target deformations across various scenarios. Experiments conducted on the SIRST-5K and IRSTD-1K datasets demonstrate that our method outperforms both the baseline YOLOv8 model and several state-of-the-art YOLOv8-based improved detection methods. The results show that, compared to the baseline model, our approach achieves mAP@[0.5:0.95] improvements of 12.3% and 16.4% on the two datasets, respectively. These results highlight the effectiveness of our proposed enhancements in improving detection accuracy and model robustness.

Traditional 2D imaging technologies are limited by the need for a large field of view and their sensitivity to small target motion. Inspired by the characteristics of insect compound eye structure, we propose an infrared bionic compound eye camera based on a small lens array. The camera is composed of 61 small lens arrays mounted on a curved spherical shell and a relay optical system. The imaging device is a high-performance cooled mid-wave infrared detector. This is an innovative design for an infrared biomimetic compound eye camera system that provides a wide field of view and all-day detection capability. Aimed to meet the specified requirements. The optical system achieves a 100% cold-membrane match between the infrared optical system and the cooled detector, and the relay optical system optimizes the large-field aberration by introducing a higher-order aspheric surface and modifying the geometric surface of the lenses. Our entire system enables an observation field angle of $$108^\circ \times 108^\circ$$108 ∘ × 108 ∘ . The experiments showed that the image quality of the system is high, each ommatidium was effective within the imaging range of the compound eye camera, resulting in an improved signal-to-noise ratio in various scenes.

To achieve rapid distance estimation and tracking of moving targets in a large field of view, this paper proposes an innovative simulation method. Using a low-cost approach, the imaging and distance measurement performance of the designed cooling-type mid-wave infrared compound-eye camera (CM-CECam) is experimentally evaluated. The compound-eye camera consists of a small-lens array with a spherical shell, a relay optical system, and a cooling-type mid-wave infrared detector. Based on the spatial arrangement of the small-lens array, a precise simulation imaging model for the compound-eye camera is developed, constructing a virtual imaging space. Distance estimation and error analysis for virtual targets are performed using the principle of stereo disparity. This universal simulation method provides a foundation for spatial design and image-plane adjustments for compound-eye cameras with specialized structures. Using the raw images captured by the compound-eye camera, a scene-specific piecewise linear mapping method is applied. This method significantly reduces the brightness contrast differences between sub-images during wide-field observations, enhancing image details. For the fast detection of moving targets, ommatidia clusters are defined as the minimal spatial constraint units. Local information at the centers of these constraint units is prioritized for processing. This approach replaces traditional global detection methods, improving the efficiency of subsequent processing. Finally, the simulated distance measurement results are validated using real-world scene data.

With the development of imaging technology, traditional two-dimensional imaging technology has difficulty meeting the observation needs of a large field of view, and has a high sensitivity to the movement of small targets. Inspired by the characteristics of the insect compound eye structure, this paper proposes an infrared bionic compound eye camera based on a small lens array. The camera consists of 61 small lens arrays mounted on a curved spherical shell and a relay optical system, and the imaging device is a high-performance cooled mid-wave infrared detector. This is an innovative design of an infrared biomimetic compound eye camera system with a wide field of view and all-day detection capability. To meet the requirement of 100% cold-membrane matching between the infrared optical system and the cooled detector, the relay optical system optimizes the large-field aberration by introducing a higher-order aspherical surface and changing the geometric surface of the lenses, and the whole system realizes an observation field angle of 108 °× 108 °. The experiments showed that the image quality of each ommatidium was good in the imaging range of the compound eye camera, and the imaging signal-to-noise ratio improved in various scenes.

Compared to conventional single-aperture infrared cameras, the bio-inspired infrared compound eye camera integrates the advantages of infrared imaging technology with the benefits of multi-aperture systems, enabling simultaneous information acquisition from multiple perspectives. This enhanced detection capability demonstrates unique performance in applications such as autonomous driving, surveillance, and unmanned aerial vehicle reconnaissance. Current single-aperture small target detection algorithms fail to exploit the spatial relationships among compound eye apertures, thereby underutilizing the inherent advantages of compound eye imaging systems. This paper proposes a low-rank and sparse decomposition method based on bio-inspired infrared compound eye image features for small target detection. Initially, a compound eye structural weighting operator is designed according to image characteristics, which enhances the sparsity of target points when combined with the reweighted $l_1$-norm. Furthermore, to improve detection speed, the structural tensor of the effective imaging region in infrared compound eye images is reconstructed, and the Representative Coefficient Total Variation method is employed to avoid complex singular value decomposition and regularization optimization computations. Our model is efficiently solved using the Alternating Direction Method of Multipliers (ADMM). Experimental results demonstrate that the proposed model can rapidly and accurately detect small infrared targets in bio-inspired compound eye image sequences, outperforming other comparative algorithms.