Query: YOLOV3

title: YOLOv3: An Incremental Improvement
abstract: We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at this https URL

title: Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R CNN and YOLOv3
abstract: Unmanned Aerial Vehicles are increasingly being used in surveillance and traffic monitoring thanks to their high mobility and ability to cover areas at different altitudes and locations. One of the major challenges is to use aerial images to accurately detect cars and count them in real time for traffic monitoring purposes. Several deep learning techniques were recently proposed based on convolution neural network (CNN) for real time classification and recognition in computer vision. However, their performance depends on the scenarios where they are used. In this paper, we investigate the performance of two state of the art CNN algorithms, namely Faster R CNN and YOLOv3, in the context of car detection from aerial images. We trained and tested these two models on a large car dataset taken from UAVs. We demonstrated in this paper that YOLOv3 outperforms Faster R CNN in sensitivity and processing time, although they are comparable in the precision metric.

title: Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving
abstract: The use of object detection algorithms is becoming increasingly important in autonomous vehicles, and object detection at high accuracy and a fast inference speed is essential for safe autonomous driving. A false positive (FP) from a false localization during autonomous driving can lead to fatal accidents and hinder safe and efficient driving. Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications. This paper proposes a method for improving the detection accuracy while supporting a real time operation by modeling the bounding box (bbox) of YOLOv3, which is the most representative of one stage detectors, with a Gaussian parameter and redesigning the loss function. In addition, this paper proposes a method for predicting the localization uncertainty that indicates the reliability of bbox. By using the predicted localization uncertainty during the detection process, the proposed schemes can significantly reduce the FP and increase the true positive (TP) thereby improving the accuracy. Compared to a conventional YOLOv3, the proposed algorithm, Gaussian YOLOv3, improves the mean average precision (mAP) by 3.09 and 3.5 on the KITTI and Berkeley deep drive (BDD) datasets, respectively. Nevertheless, the proposed algorithm is capable of real time detection at faster than 42 frames per second (fps) and shows a higher accuracy than previous approaches with a similar fps. Therefore, the proposed algorithm is the most suitable for autonomous driving applications.

title: An improved tiny yolov3 pedestrian detection algorithm
abstract: The existing real time pedestrian detection method often loses part of the detection accuracy. For this reason, we proposed a real time pedestrian detection algorithm based on tiny yolov3. The proposed method uses K means clustering on our training set to find the best priors. We improved the network structure of tiny yolov3 to make it more accurate in pedestrian detection. From the experimental results, the proposed method has higher detection accuracy under the premise of satisfying real time performance.

title: Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3 Dense
abstract: Plant disease is one of the primary causes of crop yield reduction. With the development of computer vision and deep learning technology, autonomous detection of plant surface lesion images collected by optical sensors has become an important research direction for timely crop disease diagnosis. In this paper, an anthracnose lesion detection method based on deep learning is proposed. Firstly, for the problem of insufficient image data caused by the random occurrence of apple diseases, in addition to traditional image augmentation techniques, Cycle Consistent Adversarial Network (CycleGAN) deep learning model is used in this paper to accomplish data augmentation. These methods effectively enrich the diversity of training data and provide a solid foundation for training the detection model. In this paper, on the basis of image data augmentation, densely connected neural network (DenseNet) is utilized to optimize feature layers of the YOLO V3 model which have lower resolution. DenseNet greatly improves the utilization of features in the neural network and enhances the detection result of the YOLO V3 model. It is verified in experiments that the improved model exceeds Faster R CNN with VGG16 NET, the original YOLO V3 model, and other three state of the art networks in detection performance, and it can realize real time detection. The proposed method can be well applied to the detection of anthracnose lesions on apple surfaces in orchards.

title: Automated identification of cephalometric landmarks: Part 1 Comparisons between the latest deep learning methods YOLOV3 and SSD.
abstract: To compare the accuracy and computational efficiency of two of the latest deep learning algorithms for automatic identification of cephalometric landmarks.  A total of 1028 cephalometric radiographic images were selected as learning data that trained You YOLOv3) and Single Shot Multibox Detector (SSD) methods. The number of target labeling was 80 landmarks. After the deep learning process, the algorithms were tested using a new test data set composed of 283 images. Accuracy was determined by measuring the point to point error and success detection rate and was visualized by drawing scattergrams. The computational time of both algorithms was also recorded.  The YOLOv3 algorithm outperformed SSD in accuracy for 38 of 80 landmarks. The other 42 of 80 landmarks did not show a statistically significant difference between YOLOv3 and SSD. Error plots of YOLOv3 showed not only a smaller error range but also a more isotropic tendency. The mean computational time spent per image was 0.05 seconds and 2.89 seconds for YOLOv3 and SSD, respectively. YOLOv3 showed approximately 5% higher accuracy compared with the top benchmarks in the literature.  Between the two latest deep learning methods applied, YOLOv3 seemed to be more promising as a fully automated cephalometric landmark identification system for use in clinical practice.

title: Mini YOLOv3: Real Time Object Detector for Embedded Applications
abstract: Real time scene parsing through object detection running on an embedded device is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, we redesign a lightweight network without notably reducing detection accuracy. Based on the Darknet 53, we use depth separable convolutions and pointwise group convolutions to reduce the parameter size of the network. A feature extraction backbone network with a parameter size of only 16 percent of darknet 53 is constructed. Meanwhile, in order to compensate for the degradation of accuracy, we have added a Multi Scale Feature Pyramid Network based on a simple U shaped structure to improve the performance of multi scale object detection, which called it Mini YOLOv3. It has smaller model size and fewer trainable parameters and floating point operations (FLOPs) in comparison of YOLOv3. We evaluate Mini YOLOv3 on MS COCO benchmark dataset; The parameter size of Mini YOLOv3 is only 23% of YOLOv3 and achieves comparable detection accuracy as YOLOv3 but only requires 1/2 detect time, Specifically, Mini YOLOv3 achieves mAP 50 of 52.1 at speed of 67 fps.

title: Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R CNN and YoloV3
abstract: In this paper, we address the problem of car detection from aerial images using Convolutional Neural Networks (CNN) This problem presents additional challenges as compared to car (or any object) detection from ground images because features of vehicles from aerial images are more difficult to discern. To investigate this issue, we assess the performance of two state of the art CNN algorithms, namely Faster R CNN, which is the most popular region based algorithm, and YOLOv3, which is known to be the fastest detection algorithm. We analyze two datasets with different characteristics to check the impact of various factors, such as UAV's altitude, camera resolution, and object size. The objective of this work is to conduct a robust comparison between these two cutting edge algorithms. By using a variety of metrics, we show that none of the two algorithms outperforms the other in all cases.

title: A novel YOLOv3 arch model for identifying cholelithiasis and classifying gallstones on CT images
abstract: Locating diseases precisely from medical images, like ultrasonic and CT images, have been one of the most challenging problems in medical image analysis. In recent years, the vigorous development of deep learning models have greatly improved the accuracy in disease location on medical images. However, there are few artificial intelligent methods for identifying cholelithiasis and classifying gallstones on CT images, since no open source CT images dataset of cholelithiasis and gallstones is available for training the models and verifying their performance. In this paper, we build up the first medical image dataset of cholelithiasis by collecting 223846 CT images with gallstone of 1369 patients. With these CT images, a neural network is trained to "pick up" CT images of high quality as training set, and then a novel Yolo neural network, named Yolov3 arch neural network, is proposed to identify cholelithiasis and classify gallstones on CT images. Identification and classification accuracies are obtained by 10 fold cross validations. It is obtained that our Yolov3 arch model is with average accuracy 92.7% in identifying granular gallstones and average accuracy 80.3% in identifying muddy gallstones. This achieves 3.5% and 8% improvements in identifying granular and muddy gallstones to general Yolo v3 model, respectively. Also, the average cholelithiasis identifying accuracy is improved to 86.50% from 80.75% Meanwhile, our method can reduce the misdiagnosis rate of negative samples by the object detection model.

title: A Target Detection Model Based on Improved Tiny Yolov3 Under the Environment of Mining Truck
abstract: In the open pit mine production systems, a certain number of trucks transport mine and rock between the power shovel and the unloading point. Due to the mining truck has characteristics of high height, long width and big size, it has a large blind zone and a long braking distance. Therefore, the probability of accidents in mining trucks is high, which results in huge loss of manpower, material resources and financial resources. In this paper, tiny yolov3 is used to detect obstacles in the mine, its real time performance is high enough, but the detection accuracy is not ideal. Therefore, this paper proposes an improved target detection model based on tiny yolov3. The residual network structure based on convolutional neural network is added to the tiny yolov3 structure, and the accuracy of obstacle detection is improved under the condition of real time detection. The experimental results show that compared with tiny yolov3, the detect precision of tiny yolov3 with residual structure is improved, and the detection speed is reduced slightly, there is no particular impact on the real time nature of the entire algorithm.