Query: improved yolov3

title: An Improved YOLOv3 based Neural Network for De identification Technology
abstract: In this paper, we proposed an improved YOLOv3 based neural network for De identification technology. The existing YOLOv3 is a network with fast speed and performance recently. Most surveillance system using CCD cameras simultaneously store images from cameras installed in multiple locations. In such an environment, the use of deep learning requires a method of detecting objects through a single inference engine in a plurality of image. If the inference engine hardware is used for each camera channel, the cost of building a surveillance system increases significantly. Therefore, in the field of surveillance systems, a network structure with a high detection speed is required even if the detection performance is slightly degraded. This paper proposes a method to increase the detection speed by reducing the existing YOLOv3 network Architecture. 53 feature extractors, Darknet 53, are reduced to 24 layers. Therefore, a total of 106 layers is reduced to 39. And 53 YOLOv3 box detection parts are reduced to 15 layers. In order to verify the efficiency of the proposed algorithm, the WIDER FACE dataset and its own collected dataset, we compared the performance with the existing YOLOv3 tiny and YOLOv3. As a result, the result was 87.48% mAP improved by 19.55% compared to the conventional YOLOv3 tiny. And I got a slow result of 100.5 FPS speed than the existing speed. And Object De identification technology has been applied according to the results of the detection box. Therefore, it is faster than YOLOv3, and it is similar to YOLOv3 detection accuracy, proving that it is better than YOLOv3 tiny in real time detection.

title: An Efficient and Scene Adaptive Algorithm for Vehicle Detection in Aerial Images Using an Improved YOLOv3 Framework
abstract: Vehicle detection in aerial images has attracted great attention as an approach to providing the necessary information for transportation road network planning and traffic management. However, because of the low resolution, complex scene, occlusion, shadows, and high requirement for detection efficiency, implementing vehicle detection in aerial images is challenging. Therefore, we propose an efficient and scene adaptive algorithm for vehicle detection in aerial images using an improved YOLOv3 framework, and it is applied to not only aerial still images but also videos composed of consecutive frame images. First, rather than directly using the traditional YOLOv3 network, we construct a new structure with fewer layers to improve the detection efficiency. Then, since complex scenes in aerial images can cause the partial occlusion of vehicles, we construct a context aware based feature map fusion to make full use of the information in the adjacent frames and accurately detect partially occluded vehicles. The traditional YOLOv3 network adopts a horizontal bounding box, which can attain the expected detection effects only for vehicles with small length width ratio. Moreover, vehicles that are close to each other are liable to cause lower accuracy and a higher detection error rate. Hence, we design a sloping bounding box attached to the angle of the target vehicles. This modification is conducive to predicting not only the position but also the angle. Finally, two data sets were used to perform extensive experiments and comparisons. The results show that the proposed algorithm generates the desired and excellent performance.

title: Vehicle Detection in the Aerial Infrared Images via an Improved Yolov3 Network
abstract: Owing to the great adaptability to the weak light, the infrared camera equipped on the unmanned aerial vehicle is more and more adopted to capture the aerial images. Therefore, how to fully utilize the aerial infrared images for the vehicle detection has attracted widespread attentions. However, due to the low resolution, low contrast, and few texture features of the infrared image, it is really difficult to implement the vehicle detection in the aerial infrared images. In our work, an efficient and accurate vehicle detection algorithm in aerial infrared images is proposed via an improved yolov3 network. To increase the detection efficiency, we construct a new structure of the improved yolov3 network with only 16 layers. Besides, we expand the anchor boxes to four scales to improve the detection accuracy of the small vehicles. Meanwhile, for the limitation of the infrared vehicle samples, the transfer learning is introduced to train the improved yolov3 network. Finally, the proposed algorithm is evaluated on the VIVID and NPU data sets. Experiments and comprehensive analyses demonstrate that the proposed algorithm generates satisfactory and competitive vehicle detection results.

title: Real time airplane detection algorithm in remote sensing images based on improved YOLOv3
abstract: Focusing on the airplanes in remote sensing images, a real time algorithm based on improved YOLOv3 is proposed to detect airplanes in remote sensing images. Firstly, a convolutional neural network that consists of 49 convolutional layers is proposed to detect airplanes in remote sensing images specifically. Secondly, dense connection is employed on proposed convolutional neural network, and maxpool is employed to enhance the feature transmit between dense blocks. Finally, to deal with the fact that airplanes in remote sensing images are small targets mainly, we propose to increase the scale detection from 3 to 4 and employ dense connection to merge feature map among different scales. The algorithm is trained and tested on the designed airplane dataset. The experiment results show that our algorithm obtain 96.26% on precision and 93.81% on recall.

title: An Improved YOLOV3 for Pedestrian Clothing Detection
abstract: Pedestrian clothing detection, which is dedicated to the detection of pedestrian clothing, is of great significance in pose estimation, pedestrian classification, security and so on. We propose an algorithm named YOLOV3 PCD (An Improved YOLOV3 for Pedestrian Clothing Detection) for pedestrian clothing detection, which finds out the types and positions of clothes on pedestrians. Since there is no available dataset for pedestrian clothing detection task, we build our own dataset in which most objects are large and re cluster the anchor boxes as well. In potential application fields of pedestrian clothing detection such as pose estimation and security scenarios, targets that need to be detected are usually large. So, we remove the scale used to detect small objects in the original YOLOV3. In addition, we simultaneously consider the detection of the rest medium and maximum scales, and introduce the down sampling parallel to the original YOLOV3's up sampling. Based on the above two improvements, we effectively increase the propagation and reuse of features, and improve network performance in the big object detection. In the end, for the application of embedded device in different scenarios, we prune the network to make it fast and small. Experiments show that the mAP of our proposed model reaches 91.99% which is 2% higher than the original YOLOV3 model and the number of parameters reduces to 28.74% of the original YOLOV3 model after pruning.

title: Road sludge detection and identification based on improved Yolov3
abstract: In order to deal with the challenge of the identification task of road sludge under real scenes, we propose a novel detection for road sludge detection. It combines the road sludge features extracted by the residual network with the feature maps of various scales. The swish activation function is used in the network, and GIoU loss is used as the loss of position regression. The Improved YOLOv3 experiment on our self built road sludge datasets shows superior performance in speed and accuracy and it also realized real time detection and recognition. Specially, the detection time of each frame is 45ms on the GPU (GTX 1080ti) acceleration. Furthermore, it achieved a 96.7% precise rate and a 63.5% recall rate.

title: Abnormal Detection of Substation environment based on Improved YOLOv3
abstract: In order to satisfy the requirements of power operation scene safety monitoring and lessen the human accidents, a novel framework of video surveillance for abnormal detection in substations based on artificial technology was presented. Firstly, a multi classes detector based on improved YOLOv3 was proposed to realize the detection and localization of multiple targets. Then, types of violation and defect were discriminated by secondary analysis for the workers or equipments based on detection results. Substation safety monitoring system was developed to implement environment abnormal detection, and it improved the automation and intelligentization level of unmanned substation. The experimental results demonstrate that the mAP(Mean Average Precision) reaches 0.85, which is slightly improved compared with YOLOv3. Nevertheless, the proposed algorithm is capable of real time detection as faster than 15 frames per second (fps)

title: Ship Detection: An Improved YOLOv3 Method
abstract: YOLOv3 is the state of art detector, which performs an excellent balance in detection speed and accuracy. In this paper, an improved YOLOv3 model named YOLOv3 ship is proposed for the ship detection. The main contributions to the YOLOv3 ship consists of dimension Clusters, network Improvement and embedding of the Squeeze and Excitation(SE) module. The experiments results show that the detection accuracy has been significantly improved by the YOLOv3 ship.

title: Aerial Target Detection Based on the Improved YOLOv3 Algorithm
abstract: The YOLOv3 algorithm is directly used to detect aerial targets with complex backgrounds, which leads to low detection accuracy and high missed detection rate. This paper proposes an improved YOLOv3 algorithm to detect aerial targets. Because the aerial target is small and its pixel is low, and its features are not obvious, this paper first uses the K means clustering algorithm to analyze the dataset, selecting the appropriate number and size of the anchor boxes, and then improving the network detection scale to establish the feature fusion target detection layer. In the dataset of PETS200S Tracking and DOTA, the improved YOLOv3 algorithm and the YOLOv3 algorithm are compared. The results show that the improved network can effectively detect the aerial target, and its precision and recall rate are significantly improved.

title: Object Detection Based on Improved YOLOv3 tiny
abstract: Deep learning has gradually become the mainstream object detection algorithm because of its powerful feature extraction ability and adaptive ability. However, how to guarantee the accuracy and speed is still a huge challenge in the field of object detection. This paper proposes an improved YOLOv3 tiny for object detection based on the idea of feature fusion. YOLOv3 tiny is chosen as the basic network framework to ensure the identification speed. In order to improve the poor detection accuracy of YOLOv3 tiny network, feature fusion is carried out based on Feature Pyramid Network. The last DBL (Darknetconv2d_ Batch Normalization_Leaky) output of the second scale output layer is fused with the fourth DBL output of the network. And a 52$\times$ 52 scale output is added on the basis of the original network. Experiments were carried out on our dataset. The experimental results show that compared with YOLOv3 tiny, the accuracy of the improved network structure is increased by 6.3% and the detection speed is 31.8fps, ensuring the requirements of real time detection.