## 1 Introduction  
Object detection holds a central position in the realm of computer vision, forming the bedrock for tasks such as image segmentation and object tracking. With the relentless progression of computer hardware capabilities, object detection has pervaded numerous domains, encompassing security, healthcare, and autonomous driving. It has notably catalyzed urban development by curtailing expenses and elevating societal efficacy. In everyday life, applications like facial recognition-based transactions, access control, and text recognition have seamlessly integrated into our routines. In the medical arena, object detection technology lends critical support to physicians, aiding in diagnoses and accurately pinpointing potential anomaly regions. Within industrial production, object detection methods discern product defects, facilitating automated oversight and serving as a pivotal component in assembly lines. In the military domain, these techniques offer reconnaissance potential by surveilling adversary targets through remote sensing imagery. In agriculture, object detection assumes the role of pest infestation monitoring and constitutes a cornerstone in automated harvest equipment. The ongoing evolution of object detection methodologies has instigated transformative revolutions across multifarious sectors, yielding enhancements in efficiency, precision, and automation. The objective of object detection encompasses the identification of object categories within input images and the precise delineation of their positions and boundaries using rectangular bounding boxes, coupled with associated confidence scores. While the human eye may find recognizing object categories and positions intuitive, for computers, an image translates to a set of data points. Deriving object category insights and accurately localizing objects from these data points constitutes a formidable computational task. Variables like object dimensions, orientations, illumination, and background disturbances all contribute to the intricacies of object detection performance. Hence, object detection confronts a multitude of challenges, demanding precise object localization and classification. Attaining these objectives with a harmonious interplay of accuracy and speed underscores the core of successful object detection. Furthermore, objects manifest in diverse scales, driving the pursuit of equilibrium in performance across these scales as a pressing research endeavor. The incorporation of adept attention mechanisms further augments object detection efficiency, requiring the construction of streamlined attention mechanisms that adeptly balance efficacy and computational efficiency. Conventional approaches to image object detection employ sliding window methodologies to systematically navigate through entire images. These methods are complemented by manually engineered features that possess the ability to remain invariant to factors like lighting conditions, rotation, translation, and scaling. These intricately designed features are tailored to accommodate common fluctuations in lighting and shapes. The subsequent phase involves training classifiers on these features. [PERSON] and [PERSON] [1] pioneered the Viola-Jones (VJ) detector, which extracts Haar features from images to automate facial detection through a sliding window mechanism. [PERSON] and [PERSON] [2] introduced the concept of histogram of oriented gradient (HOG) features, effectively balancing between the invariance to translation and scaling. [PERSON] et al. [3] innovated the deformable part model (DPM), which dissects object detection into various constituent detection tasks. Prominent classifiers employed include support vector machines (SVMs) and Bayesian classifiers. Conventional methodologies for image object detection are fraught with several challenges. These include the substantial computational load of sliding window mechanisms and the intricate development of handcrafted features.