## 4 Performance Analysis
### Experimental Verification and Result Analysis  
To solve the small object problem of the object detection algorithm, this paper uses an improved YOLO-Tiny object detection algorithm fused with the VaryBlock module. Since the objects in the COCO dataset and the TUM dataset are similar in size, the Stochastic Gradient Descent (SGD) method is used to pre-train RGB images (labeled) from the COCO dataset and TUM dataset. Then, we load the obtained weight file into the model as the weight before training and finally verify it in the RGB image of TUM RGB-D. The annotation tool uses Labelimg. Labelimg is a graphical image annotation tool that labels object bounding boxes in images. Our experiment is conducted on an Ubuntu PC using an Intel I7-9700K CPU, 16 GB DDR4 3000, and NVIDIA GTX 750 with 4 GB of memory. During the experiment, the VaryBlock module is added to the network. In addition, before the start of the training phase, this paper first uses WGAN and traditional methods to expand the training set and uses the K-means++ clustering algorithm to cluster the bounding boxes of the training set to obtain the preset bounding box. Each training image is randomly sampled to 70% for training and 30% for validation to make the detector more resilient to input objects of various sizes and shapes. In the training step, the experiment trains a total of 10 categories across 45,000 iterations, with basic learning, momentum, and weight attenuation coefficients of 0.001, 0.9, and 0.0005, respectively. With a batch size of 64 and a subdivision size of 32, we apply the Stochastic Gradient Descent method. To lessen the burden of occupying memory, each iteration randomly selects 64 samples from all training sets to engage in training; subsequently, all batch samples are separated into 32 parts and delivered to the network to participate in training. This research also use the WGAN approach to broaden the scope of the training set. The front end of the model will be changed to 416 \(\times\) 416 pixels and three channels for any input image size, and the final output will be the same size as the original image. The MS COCO dataset and RGB images from the TUM dataset were used as the training and validation sets in this work. As shown in Table 4, we apply the same enhancement to YOLOv3-Tiny and YOLOv4-Tiny, respectively, and compare the results. Our method shows a significant improvement over the original YOLO-Tiny, and it is comparable to other similar methods, such as SSD(300), RCNN, Faster RCNN, and YOLOv3. To train our approach, we utilized the COCO2017 Trainval + TUM train dataset, and to test multiple methods, we used the COCO2017 test-dev + TUM test dataset. Compared with the original YOLOv3-Tiny, the mAP of our method based on YOLOv3-Tiny is improved from 55.3% to 60.4%, especially the APS is increased by 8%, but the APM and APL are not improved very much, so our method is more targeted for small objects detection. The detection results are comparable with YOLOv4-Tiny, and some indexes even exceed YOLOv4-Tiny. However, the FPS decreases from 74 to 65, but it still meets the requirements of the real-time system. Furthermore, the improved network based on YOLOv4-Tiny has the same improvement effect in our experiment. Figure 7 shows some detection results of the MS COCO2017 dataset with our method. We have performed some experiments to verify the improvement effect of the VaryBlock module on the original YOLOv3-Tiny network. First, we add VaryBlock 1 to VaryBlock 3 into the initial network of YOLOv3-Tiny and compare it with the initial network. Then, we add VaryBlock 4 to VaryBlock 6 for comparison again. The mAP, APS, and FPS in the experiment are shown in Table 5.