## 2 Problem Description  
Object detection constitutes a ubiquitous domain within the realm of computer vision. Throughout its evolutionary trajectory, a central hurdle has involved crafting more potent feature extraction modules rooted in deep learning. In the nascent phases of object detection networks, comparatively straightforward convolutional neural networks were integrated as feature extraction modules within the bedrock network architecture. As a result, the foundational network architecture was constrained to extracting features solely at a singular scale. While these features showcased robust information representation capacities at a macroscopic level, the escalating requisites for heightened object detection performance accentuated the inadequacy of these features operating at a solitary scale. In the course of convolutional pooling, information pertaining to diminutive objects experienced a gradual erosion within the confines of the one-scale feature maps. Subsequently, the significance of multi-scale feature extraction networks gained prominence, exemplified by models such as UNet [16] and FPN [17]. At present, the extensively employed FPN architecture has exhibited enhanced performance in object detection, as demonstrated in the case of Sparse R-CNN [18]. Through the fusion of multi-scale feature maps, FPN facilitates the reciprocal exploitation of both high-level and low-level features. High-level features augment the semantic representation of low-level features, while low-level features amplify intricate details grounded in high-level features. Nevertheless, considering FPN's multi-scale fusion principle, directly amalgamating features from distinct levels featuring pronounced semantic disparities may not yield optimal results. Moreover, during the utilization of FPN, direct fusion of features spanning the highest and lowest levels could potentially engender information loss. The proposed enhancements in this study are predicated on the Sparse R-CNN object detection algorithm, which represents a distinctive approach. Sparse R-CNN, functioning as a two-stage object detection technique, deviates from the conventional paradigm of such algorithms. Notably, its Region Proposal Network (RPN) engenders a fixed ensemble of 100 candidate boxes. Subsequently, these designated boxes traverse a Dynamic Instance Interactive Head (DIIHead) module. For every individual candidate box, the RoIAlign algorithm orchestrates the extraction of region-specific features, subsequently facilitating the execution of tasks pertaining to target classification and positional regression. The processed boxes subsequently undergo a succession of six iterations through the DIIHead module, progressively refining the initial 100 candidate boxes and culminating in the derivation of the ultimate output. Of notable significance, the Sparse R-CNN method circumvents the necessity for a post-processing step such as nonmaximum suppression (NMS). This sets it apart from conventional two-stage object detection techniques like Faster R-CNN, which generate a voluminous array of candidate boxes exceeding hundreds of thousands during the Region Proposal Network (RPN) phase. In stark contrast, Sparse R-CNN judiciously employs a sparse collection of 100 candidate boxes within the output feature map, each imbued with learnable attributes. This limited set of 100 candidates places a higher reliance on the extraction of high-level features. Notably, the feature pyramid network (FPN) encounters a diminution of high-level features as a consequence of fusion. Consequently, the direct adoption of FPN as the multi-scale feature extraction module for Sparse R-CNN remains poised for enhancements. Within Sparse R-CNN, the ultimate goal is to derive conclusive outcomes from the sparse set of 100 learnable candidate boxes. To ensure a closer alignment between the ascertained classes and the actual target classes, an improved classification loss function becomes imperative.