More recently, deep-learning methods and, above all, convolutional neural networks (CNNs) have [9] optimized the performance of ML methods in landslide detection by using Dempster–Shafer theory (DST) based on the probabilistic output from object-based SVM, K-nearest neighbor (KNN) and RF methods. Automatic annotation of simulated images to generate bounding box coordinates. For example, in VGG16, if the object of interest occupies a 32  32 size, it will be presented at most 1 pixel after 5 times of going through the pooling block. However, tissue has least contribution with the lowest AP originally affected by the number of data. In addition to the comparative accuracy, other comparisons are also provided to make our objective and clear assessment results. In [19], Torralba et al. However, it does not publish the labels for test set to evaluate, and the views of images are topdown which is not our case. This shows that if objects are completely separated into different scales, the RoI pooling does not work well with smaller objects and ones in VOC_WH20. The architecture of Fast R-CNN is trained end-to-end with a multitask loss. Finally, using the class-specific linear SVM classifier behind the last layer is to classify regions to consider if there are any objects and what the objects are. As a result, performance of object detection has recently had significant improvements. When it comes to backbones, we have to concern about the data to choose a reasonable backbone to combine with the methods. We evaluate three state-of-the-art models including You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Faster R-CNN with related trade-off factors, i.e., accuracy, execution time, and resource constraints. The huge contribution of Fast R-CNN is that it proposes a new training method that fixes the drawbacks of R-CNN and SPP-net, while increasing their running time and accuracy rate. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Sign up here as a reviewer to help fast-track new submissions. In Text: Zero Shot Translation, Sentiment Classification. Then, it combines 6 convolutional layers to make prediction. The VGG16 backbone has an impressive outcome rather than strong backbones such as ResNet or ResNeXT. In addition, YOLOv2 has a fluctuation with those objects in VOC_WH20. Besides, resizing the input to the low 227  227 is a problem affecting small objects which are easy to deform or even lose information as changing the resolution far from its original sizes. Similarly, ResNet-50-FPN and ResNet-50-C4 are chosen to consider. It illustrates that real-time object detection, applied to the most popular vision-based applications in real world, is really indispensable. Object detection is a computer vision technique for locating instances of objects in images or videos. Besides, most of the state-of-the-art detectors, both in one-stage and two-stage approaches, have struggled with detecting small objects. For example, according to the statistics in [13], mouse is a major class significantly contributing to mAP in Table 3 with the highest number of instances and images as well. We still make our evaluation on 2 datasets namely, small object dataset [13] and our filtered dataset from PASCAL VOC 2007 [11] with criteria such as accuracy, speed of processing, and resource consumption as well. However, models in the two-stage approach have their reputation of region-based detectors which have high accuracy but are too low in speed to apply them to real world. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in, K. Židek, A. Hosovsky, J. Pitel’, and S. Bednár, “Recognition of assembly parts by convolutional neural networks,” in, K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” in. Although deep models belonging to detection originally tend to solve problems relating to general object detection, they still work at a particular level to the success of small object detection. 1.6G to an evaluation of deep learning methods for small object detection for testing, so it is arduous when differentiating small objects is more stable than SSD RetinaNet. To base on or develop from it result most likely in a.. In threat detection techniques for object detection ResNet-101 to ResNet-152 about 1–2 % in threat detection for... The previous approaches just specify to focus on estimating predictive distributions for bounding box regression output with … overview localization! 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