## 4 Experiments and Analysis  
### Datasets and Evaluation Metrics  
This section outlines the datasets employed in the experiments and the evaluation criteria utilized. MS COCO 2017: The Microsoft Common Objects in Context (MS COCO) dataset [21] stands as a cornerstone in computer vision tasks, including object detection and semantic segmentation. It gained prominence following the discontinuation of the ImageNet competition in 2017. COCO has attracted contributions from global tech giants like Google, Microsoft, Facebook, and esteemed research institutions. It boasts a comprehensive object detection dataset drawn from intricate everyday scenes, featuring over 200,000 images and 80 categories. The training set of COCO 2017 comprises 118,287 images, and the validation set contains 5000 images. MS COCO 2014: Introduced in 2014 [21], the COCO dataset of 2014 diverges in content from its 2017 counterpart. It incorporates a training set of 82,783 images, a validation set of 40,504 images, and a test set encompassing 40,775 images. Pascal VOC 2007 [22]: Pascal VOC (Pattern Analysis, Statistical Modeling, and Computational Learning Visual Object Classes) serves as a benchmark dataset for object detection, encompassing image classification, object detection, and semantic segmentation tasks. The 2007 edition comprises 20 categories and a total of 9963 annotated images. The dataset is partitioned into a training set of 5011 images and a test set of 4952 images. Pascal VOC 2012: Representing the concluding year of this esteemed computer vision challenge, Pascal VOC 2012 features image classification, object detection, object segmentation, and action recognition as its principal tasks. It encompasses 11,530 images with 27,450 object annotations and 6929 segmentation annotations. Evaluation Criteria: For object detection evaluation, the COCO dataset's evaluation metrics are employed due to the conversion of Pascal VOC datasets into COCO format for experimentation. The primary evaluation metric for object detection in the COCO dataset is the mean average precision (mAP), a measure of average accuracy. Given that object detection involves generating detection boxes, calculating the average precision entails a series of steps. The average AP values are determined for detecting diverse classes of objects prior to assessing classification accuracy. Throughout COCO evaluation, multiple intersection over union (IoU) thresholds are set, often spanning from 0.5 to 0.95 with increments of 0.05. For each IoU threshold, the average precision is computed for each object class. Ultimately, the obtained AP values across various IoU thresholds are averaged to yield the comprehensive mAP score. When computing the mean mAP, a crucial metric at play is intersection over union (IoU), employed to gauge the intersection extent between a detected bounding box and its corresponding ground truth bounding box. While assessing the COCO dataset, IoU thresholds, frequently set at levels like 0.5 and 0.75, come into play. If the intersection over union value between the network's output detection box and the actual ground truth bounding box exceeds the threshold, COCO deems the detection as accurate. A comprehensive illustration of the IoU calculation process is depicted in Figure 6.  
IoU stands as a pivotal metric in evaluating the efficacy of object detection, as it gauges the alignment between the predicted detection box and the true ground truth box, thereby indicating the accuracy of the detection. This assessment is based on the comparison of the predicted detection box, denoted as \(area_{1}\), and the actual annotated boundary box for the target, referred to as \(area_{2}\), with their intersection represented as \(area_{3}\). Mathematically, IoU is computed as follows:  
\[IoU=\frac{S_{area3}}{S_{area1}+S_{area2}-S_{area3}} \tag{15}\]  
In tandem with IoU calculation, the evaluation process also incorporates classification attributes, encompassing the counts of true positives (TPs), false positives (FPs), false  
tives (FNs), and true negatives (TNs).