## 1 Introduction  
Object detection is a crucial task within the domain of deep learning computer vision, which aims to recognize a particular object within an image or video, pinpointing both its location and category [1]. Notably, significant progress has been achieved in object detection through convolutional neural networks (CNNs), especially in visible light conditions where the algorithm performs exceptionally well. Nevertheless, under conditions of low visibility and extreme weather, object detection is inevitably affected by various factors, which interfere with the identification and localization of the target object, suffering from problems such as missed detection and false alarms in practical applications. This leads to a sharp decline in performance. To tackle this puzzle, infrared detection technology has emerged, finding widespread use in diverse domains like cellular tumor diagnosis [2], privacy preservation [3], marine rescue [4, 5], UAV navigation [6, 7], and autonomous driving [8, 9]. As a result, detecting infrared objects in real time with precision within low-contrast conditions has become a challenge to solve. Traditional infrared target detection algorithms exhibit instability under such extreme conditions, making infrared detection complicated and elusive. Researchers have made efforts to overcome these difficulties, and some advanced research results have been achievedin the field of infrared target detection based on deep learning [10]. [PERSON] et al. [11] proposed guided attention to color thermal infrared images in a richer semantic encoding way to enable the perception of the environment at night. However, this novel colorization strategy has insufficient generalization ability for different scenes and lighting conditions. Colorization may distort the colors in the image, creating confusion and thus reducing accuracy. [PERSON] et al. [12] attempted to use optical images to compensate for the missing information in infrared images by proposing an integrated detector to fuse the features of thermal and multiple RGB modalities. This competitive approach, however, introduces noise and inconsistency among the data from different modalities, which diminishes the quality of the integrated information. [PERSON] et al. [13] achieved the fusion of encoding and decoding features of input features by using the features generated by a deconvolution scaling decoder, but, in fact, the use of deconvolution layers leads to the loss of spatial information. [PERSON] et al. [14] introduced a contrast enhancement coefficient as the weighted part of the loss function, aiming to boost the accuracy of small infrared targets. Yet, such adjustment creates a more complicated loss function, which relies too much on prior assumptions and is effective in some specific scenarios but cannot adapt to all intricate scenarios. Although the above methods have achieved leading results in some environments, distinguishing between target and background in realistic, involved, and diverse scenes where color and texture information are lacking in infrared images is still a difficult task, and the performance o the developed methods is often unsatisfactory in practical applications. For this reason, in the face of complex scenes of infrared target detection, it is urgent to establish a powerful and robust model to advance the ability of feature expression and create new breakthroughs for infrared exploration in intricate environments. In general, object detection can be achieved through two primary detection strategies. The first strategy follows a two-stage approach, with faster R-CNN [15] serving as an example. This approach comprises two key phases: initially, potential object regions are generated by the algorithm, and then the generated candidate regions are classified and adjusted more finely by the CNN. While this strategy offers heightened accuracy, it tends to be time-consuming and not suitable for practical scenarios like unmanned driving.