Recently, the field of vehicle-mounted visual intelligence technology has witnessed a surge of interest in pedestrian detection. Existing algorithms for dense pedestrian detection at intersections face challenges such as high computational weight, complex models that are difficult to deploy, and suboptimal detection accuracy for small targets and highly occluded pedestrians. To address these issues, this paper proposes an improved lightweight multi-scale pedestrian detection algorithm, YOLOv8-CB. The algorithm introduces a lightweight cascade fusion network, CFNet (cascade fusion network), and a CBAM attention module to improve the characterization of multi-scale feature semantics and location information, and it superimposes a bidirectional weighted feature fusion path BIFPN structure to fuse more effective features and improve pedestrian detection performance. It is experimentally verified that compared with the YOLOv8n algorithm, the accuracy of the improved model is increased by 2.4%, the number of model parameters is reduced by 6.45%, and the computational load is reduced by 6.74%. The inference time for a single image is 10.8 ms. The cascade fusion algorithm YOLOv8-CB has higher detection accuracy and is a lighter model for multi-scale pedestrian detection in complex scenes such as streets or intersections. This proposed algorithm presents a valuable approach for device-side pedestrian detection with limited computational resources.

YOLOv8-CB: Dense Pedestrian Detection Algorithm Based on In-Vehicle Camera

As a flexible passive 3D sensing means, unsupervised learning of depth from monocular videos is becoming an important research topic. It utilizes the photometric errors between the target view and the synthesized views from its adjacent source views as the loss instead of the difference from the ground truth. Occlusion and scene dynamics in real-world scenes still adversely affect the learning, despite significant progress made recently. In this paper, we show that deliberately manipulating photometric errors can efficiently deal with these difficulties better. We first propose an outlier masking technique that considers the occluded or dynamic pixels as statistical outliers in the photometric error map. With the outlier masking, the network learns the depth of objects that move in the opposite direction to the camera more accurately. To the best of our knowledge, such cases have not been seriously considered in the previous works, even though they pose a high risk in applications like autonomous driving. We also propose an efficient weighted multi-scale scheme to reduce the artifacts in the predicted depth maps. Extensive experiments on the KITTI dataset and additional experiments on the Cityscapes dataset have verified the proposed approach's effectiveness on depth or ego-motion estimation. Furthermore, for the first time, we evaluate the predicted depth on the regions of dynamic objects and static background separately for both supervised and unsupervised methods. The evaluation further verifies the effectiveness of our proposed technical approach and provides some interesting observations that might inspire future research in this direction.

Unsupervised Monocular Depth Perception: Focusing on Moving Objects

Detecting and tracking dynamic objects in a scene using point cloud data collected by LiDAR and estimating the motion state of objects with high accuracy are challenges for autonomous driving technology. In this study, a motion detection method based on point cloud registration is investigated to detect motion through the overlapping relationship between source and target point clouds after registration and extract moving objects using clustering and scale analysis by combining the object information of interest acquired by deep learning networks. Next, object association is achieved by object motion information and geometric and texture features. Then, a point cloud registration method flow is designed to estimate the motion state of the object with high accuracy by point cloud registration. The detection, tracking and estimation of the accurate motion state of moving objects are achieved. 

High-Precision Motion Detection and Tracking Based on Point Cloud Registration and Radius Search

Cereal and oil video surveillance data play a vital role in food traceability, which not only helps to ensure the quality and safety of food, but also helps to improve the efficiency and transparency of the supply chain. Traditional video surveillance systems mainly adopt a centralized storage mode, which is characterized by the deployment of multiple monitoring nodes and a large amount of data storage. It is difficult to guarantee the data security, and there is an urgent need for a solution that can achieve the safe and efficient storage of cereal and oil video surveillance data. This study proposes a blockchain-based abnormal data storage model for cereal and oil video surveillance. The model introduces a deep learning algorithm to process the cereal and oil video surveillance data, obtaining images with abnormal behavior from the monitoring data. The data are stored on a blockchain after hash operation, and InterPlanetary File System (IPFS) is used as a secondary database to store video data and alleviate the storage pressure on the blockchain. The experimental results show that the model achieves the safe and efficient storage of cereal and oil video surveillance data, providing strong support for the sustainable development of the cereal and oil industry. 

Research on Blockchain-Based Cereal and Oil Video Surveillance Abnormal Data Storage

The 3-D object detection is a crucial task for autonomous driving and robotics. However, it faces various challenges attributed to the complexity and sparsity of 3-D data. Light detection and ranging (LiDAR) and camera are two common sensors that provide complementary information for 3-D object detection. LiDAR provides accurate depth and shape information, while camera captures rich semantic and texture information. However, most existing methods for LiDAR–camera fusion either ignore the frequency-domain information or use simple concatenation or elementwise operations to fuse the features, which may not fully realize the potential of both modalities. This article proposes frequency-aware LiDAR–camera fusion networks (FAFNs) that leverage the frequency-domain information to enhance the LiDAR and camera feature maps. Specifically, we design a frequency-aware feature enhancement module (FAFEM) that adaptively learns the frequency importance of each modality and assigns different weights to different frequency bands. We also design a frequency-aware multiscale query initialization strategy that enriches the object query with frequencywise information and multiscale variations. We conduct the evaluation of our FAFNs using two well-established benchmarks for 3-D object detection, nuScenes and Waymo, which demonstrates its competitive performance over existing state-of-the-art methods.

FAFNs: Frequency-Aware LiDAR-Camera Fusion Networks for 3D Object Detection

Accurate detection of independent moving objects is the key to ensure the safety of driverless vehicles during travelling. In this study, a moving object detection method based on a vehicle-mounted binocular camera is proposed. Firstly, a feature matching points selection strategy is designed for high accuracy camera ego-motion estimation. Then, the residual optical flow filed generated only by moving objects is estimated by taking into account the camera ego-motion and global mixed optical flow. A dynamic threshold segmentation method based on disparity is proposed to separate the moving regions from the residual optical flow field. Finally, 3D and 2D information are combined for extracting each single moving object from moving regions. The innovation of the proposed method is that it can detect any type and any size of moving object theoretically by using the dense motion information of the images. By taking the data in KITTI database as the test samples, the detection precision of the moving objects using the proposed algorithm can reach up to 91% without considering tracking strategy.

Independent Moving Object Detection Based on a Vehicle Mounted Binocular Camera

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Independent Moving Object Detection Based on a Vehicle Mounted Binocular Camera