Current deep learning methods seldom consider the effects of small pedestrian ratios and considerable differences in the aspect ratio of input images, which results in low pedestrian detection performance. This study proposes the ratio-and-scale-aware YOLO (RSA-YOLO) method to solve the aforementioned problems. The following procedure is adopted in this method. First, ratio-aware mechanisms are introduced to dynamically adjust the input layer length and width hyperparameters of YOLOv3, thereby solving the problem of considerable differences in the aspect ratio. Second, intelligent splits are used to automatically and appropriately divide the original images into two local images. Ratio-aware YOLO (RA-YOLO) is iteratively performed on the two local images. Because the original and local images produce low- and high-resolution pedestrian detection information after RA-YOLO, respectively, this study proposes new scale-aware mechanisms in which multiresolution fusion is used to solve the problem of misdetection of remarkably small pedestrians in images. The experimental results indicate that the proposed method produces favorable results for images with extremely small objects and those with considerable differences in the aspect ratio. Compared with the original YOLOs (i.e., YOLOv2 and YOLOv3) and several state-of-the-art approaches, the proposed method demonstrated a superior performance for the VOC 2012 comp4, INRIA, and ETH databases in terms of the average precision, intersection over union, and lowest log-average miss rate.

Ratio-and-Scale-Aware YOLO for Pedestrian Detection

Extracting vehicle information is of great significance to the construction of the Internet of Vehicles (IoV). Vehicle logo detection (VLD) technology can effectively extract vehicle information. Due to the complex traffic environment, existing methods have difficulty to accurately detect the vehicle logo, especially when the vehicle logo has motion blur. To alleviate these problems, a new approach is proposed to detect vehicle logo under motion blur with the combination of Filter-DeblurGAN and VL-YOLO. Filter-DeblurGAN possesses a judgment mechanism, which can determine whether the image needs to be deblurred based on the degree of blur of each image. It can also deblur images with arbitrary resolution. Filter-DeblurGAN solves the defect that DeblurGAN lacks the judgment mechanism and is too harsh on resolution. It can be directly applied to the deblurring process of VLD. We also propose a method using VL-YOLO and Eliminate Outliers Cluster (EOC) algorithm to further improve the detection accuracy. VL-YOLO achieves accurate detection of the vehicle logo by constructing a deeper multi-scale detection network and using the initial candidate boxes provided by EOC algorithm. The EOC algorithm is not affected by outliers. In order to further promote the research of VLD, we have labeled a new vehicle logo dataset named LOGO-17, which contains 17 different categories of vehicle logos. The experimental results demonstrate that the proposed method achieves good detection accuracy in the environment of motion blur, and outperforms existing methods.

Detecting Motion Blurred Vehicle Logo in IoV Using Filter-DeblurGAN and VL-YOLO

Deep convolutional neural network for enhancing traffic sign recognition developed on Yolo V4

Manual harvesting of loose fruits in the oil palm plantation is both time consuming and physically laborious. Automatic harvesting system is an alternative solution for precision agriculture which requires accurate visual information of the targets. Current state-of-the-art one-stage object detection method provides excellent detection accuracy; however, it is computationally intensive and impractical for embedded system. This paper proposed an improved YOLO model to detect oil palm loose fruits from unmanned aerial vehicle images. In order to improve the robustness of the detection system, the images are augmented by brightness, rotation, and blurring to simulate the actual natural environment. The proposed improved YOLO model adopted several improvements; densely connected neural network for better feature reuse, swish activation function, multi-layer detection to enhance detection on small targets and prior box optimization to obtain accurate bounding box information. The experimental results show that the proposed model achieves outstanding average precision of 99.76% with detection time of 34.06 ms. In addition, the proposed model is also light in weight size and requires less training time which is significant in reducing the hardware costs. The results exhibit the superiority of the proposed improved YOLO model over several existing state-of-the-art detection models.

Automatic detection of oil palm fruits from UAV images using an improved YOLO model

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12181

An optimized YOLO-based object detection model for crop harvesting system

Currently, the conventional license plate location method fails to detect the license plate under complex road environments such as severe weather conditions and viewpoint changes. Besides, it is difficult for license plate location method based on machine learning to precisely locate the area of license plate. Moreover, license plate location method may incorrectly detect similar objects such as billboards and road signs as license plates. To alleviate these problems, this article proposes a new approach to vehicle license plate location based on new model YOLO-L and plate pre-identification. The new model improves in two aspects to precisely locate the area of license plate. First, it uses k-means++ clustering algorithm to select the best number and size of plate candidate boxes. Second, it modifies the structure and depth of YOLOv2 model. Plate pre-identification algorithm can effectively distinguish license plates from similar objects. The experimental results show that authors' proposed method not only achieves a precision of 98.86% and a recall of 98.86%, which outperforms the existing methods, but also has high efficiency in real time.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2018.6449

Qi Wang

Papers

New approach to vehicle license plate location based on new model YOLO-L and plate pre-identification