ECDet: an efficient convolutional network for real-time object detection

Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of five major image processing algorithms: Region-based Fully Convolutional Network (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once v4 (YOLOv4). In this comparative analysis, we used a large-scale Berkeley Deep Drive (BDD100K) dataset. Their strengths and limitations are analyzed based on parameters such as accuracy (with/without occlusion and truncation), computation time, precision-recall curve. The comparison is given in this article helpful in understanding the pros and cons of standard deep learning-based algorithms while operating under real-time deployment restrictions. We conclude that the YOLOv4 outperforms accurately in detecting difficult road target objects under complex road scenarios and weather conditions in an identical testing environment.

Download Full-text

Object detection in real time based on improved single shot multi-box detector algorithm

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01826-x ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Ashwani Kumar ◽

Zuopeng Justin Zhang ◽

Hongbo Lyu

Keyword(s):

Neural Networks ◽

Object Detection ◽

Real Time ◽

Convolutional Neural Networks ◽

Single Layer ◽

Single Shot ◽

Convolutional Network ◽

Detection Techniques ◽

Computational Performance ◽

Speed Up

Abstract In today’s scenario, the fastest algorithm which uses a single layer of convolutional network to detect the objects from the image is single shot multi-box detector (SSD) algorithm. This paper studies object detection techniques to detect objects in real time on any device running the proposed model in any environment. In this paper, we have increased the classification accuracy of detecting objects by improving the SSD algorithm while keeping the speed constant. These improvements have been done in their convolutional layers, by using depth-wise separable convolution along with spatial separable convolutions generally called multilayer convolutional neural networks. The proposed method uses these multilayer convolutional neural networks to develop a system model which consists of multilayers to classify the given objects into any of the defined classes. The schemes then use multiple images and detect the objects from these images, labeling them with their respective class label. To speed up the computational performance, the proposed algorithm is applied along with the multilayer convolutional neural network which uses a larger number of default boxes and results in more accurate detection. The accuracy in detecting the objects is checked by different parameters such as loss function, frames per second (FPS), mean average precision (mAP), and aspect ratio. Experimental results confirm that our proposed improved SSD algorithm has high accuracy.

Download Full-text

Real-Time Object Detection in Remote Sensing Images Based on Visual Perception and Memory Reasoning

Electronics ◽

10.3390/electronics8101151 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1151 ◽

Cited By ~ 4

Author(s):

Xia Hua ◽

Xinqing Wang ◽

Ting Rui ◽

Dong Wang ◽

Faming Shao

Keyword(s):

Remote Sensing ◽

Visual Perception ◽

Object Detection ◽

Real Time ◽

Detection Accuracy ◽

Small Object ◽

Remote Sensing Images ◽

Feature Maps ◽

Convolutional Network ◽

Fully Convolutional Network

Aiming at the real-time detection of multiple objects and micro-objects in large-scene remote sensing images, a cascaded convolutional neural network real-time object-detection framework for remote sensing images is proposed, which integrates visual perception and convolutional memory network reasoning. The detection framework is composed of two fully convolutional networks, namely, the strengthened object self-attention pre-screening fully convolutional network (SOSA-FCN) and the object accurate detection fully convolutional network (AD-FCN). SOSA-FCN introduces a self-attention module to extract attention feature maps and constructs a depth feature pyramid to optimize the attention feature maps by combining convolutional long-term and short-term memory networks. It guides the acquisition of potential sub-regions of the object in the scene, reduces the computational complexity, and enhances the network’s ability to extract multi-scale object features. It adapts to the complex background and small object characteristics of a large-scene remote sensing image. In AD-FCN, the object mask and object orientation estimation layer are designed to achieve fine positioning of candidate frames. The performance of the proposed algorithm is compared with that of other advanced methods on NWPU_VHR-10, DOTA, UCAS-AOD, and other open datasets. The experimental results show that the proposed algorithm significantly improves the efficiency of object detection while ensuring detection accuracy and has high adaptability. It has extensive engineering application prospects.

Download Full-text