Road object detection: a comparative study of deep learning-based algorithms

With the continuous exploration of marine resources, underwater artificial intelligent robots play an increasingly important role in the fish industry. However, the detection of underwater objects is a very challenging problem due to the irregular movement of underwater objects, the occlusion of sand and rocks, the diversity of water illumination, and the poor visibility and low color contrast in the underwater environment. In this article, we first propose a real-world underwater object detection dataset (UODD), which covers more than 3K images of the most common aquatic products. Then we propose Channel Sharpening Attention Module (CSAM) as a plug-and-play module to further fuse high-level image information, providing the network with the privilege of selecting feature maps. Fusion of original images through CSAM can improve the accuracy of detecting small and medium objects, thereby improving the overall detection accuracy. We also use Water-Net as a preprocessing method to remove the haze and color cast in complex underwater scenes, which shows a satisfactory detection result on small-sized objects. In addition, we use the class weighted loss as the training loss, which can accurately describe the relationship between classification and precision of bounding boxes of targets, and the loss function converges faster during the training process. Experimental results show that the proposed method reaches a maximum AP of 50.1%, outperforming other traditional and state-of-the-art detectors. In addition, our model only needs an average inference time of 25.4 ms per image, which is quite fast and might suit the real-time scenario.

Underwater Species Detection using Channel Sharpening Attention

Encapsulation of cells inside microfluidic droplets is central to several applications involving cellular analysis. Although, theoretically the encapsulation statistics are expected to follow a Poisson distribution, experimentally this may not be achieved due to lack of full control of the experimental variables and conditions. Therefore, there is a need for automatic detection of droplets and cell count enumeration within droplets so a process control feedback to adjust experimental conditions can be implemented. In this study, we use a deep learning object detector called You Only Look Once (YOLO), an influential class of object detectors with several benefits over traditional methods. This paper investigates the application of both YOLOv3 and YOLOv5 object detectors in the development of an automated droplet and cell detector. Experimental data was obtained from a microfluidic flow focusing device with a dispersed phase of cancer cells. The microfluidic device contained an expansion chamber downstream of the droplet generator, allowing for visualization and recording of cell-encapsulated droplet images. In the procedure, a droplet bounding box is predicted, then cropped from the original image for the individual cells to be detected through a separate model for further examination. The system includes a production set for additional performance analysis with Poisson statistics while providing an experimental workflow with both droplet and cell models. The training set is collected and preprocessed before labeling and applying image augmentations, allowing for a generalizable object detector. Precision and recall were utilized as a validation and test set metric, resulting in a high mean average precision (mAP) metric for an accurate droplet detector. To examine model limitations, the predictions were compared to ground truth labels, illustrating that the YOLO predictions closely matched with the droplet and cell labels. Furthermore, it is demonstrated that droplet enumeration from the YOLOv5 model is consistent with hand counted ratios and the Poisson distribution, confirming that the platform can be used in real-time experiments for cell encapsulation optimization.

Deep learning detector for high precision monitoring of cell encapsulation statistics in microfluidic droplets.

Advances in deep learning have led to a resurgence of interest in video analytics. In an exploratory video analytics pipeline, a data scientist often starts by searching for a global trend and then iteratively refines the query until they identify the desired local trend. These queries tend to have overlapping computation and often differ in their predicates. However, these predicates are computationally expensive to evaluate since they contain user-defined functions (UDFs) that wrap around deep learning models. In this paper, we present EVA, a video database management system (VDBMS) that automatically materializes and reuses the results of expensive UDFs to facilitate faster exploratory data analysis. It differs from the state-of-the-art (SOTA) reuse algorithms in traditional DBMSs in three ways. First, it focuses on reusing the results of UDFs as opposed to those of sub-plans. Second, it takes a symbolic approach to analyze predicates and identify the degree of overlap between queries. Third, it factors reuse into UDF evaluation cost and uses the updated cost function in critical query optimization decisions like predicate reordering and model selection. Our empirical analysis of EVA demonstrates that it accelerates exploratory video analytics workloads by 4x with a negligible storage overhead (1.001x). We demonstrate that the reuse algorithm in EVA complements the specialized filters adopted in SOTA VDBMSs.

/pdf/eva-a-symbolic-approach-to-accelerating-exploratory-video-2r5arwsd.pdf

EVA: A Symbolic Approach to Accelerating Exploratory Video Analytics with Materialized Views

Pedestrian detection is vitally important in many computer vision tasks but still suffers from some problems, such as illumination and occlusion if only the RGB image is exploited, especially in outdoor and long-range scenes. Combining RGB with depth information acquired by 3D sensors may effectively alleviate these problems. Therefore, how to utilize depth information and how to fuse RGB and depth features are the focus of the task of RGB-D pedestrian detection. This paper first improves the most commonly used HHA method for depth encoding by optimizing the gravity direction extraction and depth values mapping, which can generate a pseudo-color image from the depth information. Then, a two-branch feature fusion extraction module (TFFEM) is proposed to obtain the local and global features of both modalities. Based on TFFEM, an RGB-D pedestrian detection network is designed to locate the people. In experiments, the improved HHA encoding method is twice as fast and achieves more accurate gravity-direction extraction on four publicly-available datasets. The pedestrian detection performance of the proposed network is validated on KITTI and EPFL datasets and achieves state-of-the-art performance. Moreover, the proposed method achieved third ranking among all published works on the KITTI leaderboard. In general, the proposed method effectively fuses RGB and depth features and overcomes the effects of illumination and occlusion problems in pedestrian detection.

/pdf/3d-sensor-based-pedestrian-detection-by-integrating-improved-dw6k0hpp.pdf

3D Sensor Based Pedestrian Detection by Integrating Improved HHA Encoding and Two-Branch Feature Fusion

ultralytics/yolov3: v9.5.0 - YOLOv5 v5.0 release compatibility update for YOLOv3

ultralytics/yolov3: v9.1 - YOLOv5 Forward Compatibility Updates

ultralytics/yolov3: v9.0 - YOLOv5 Forward Compatibility Release

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ultralytics/yolov3: v9.5.0 - YOLOv5 v5.0 release compatibility update for YOLOv3

ultralytics/yolov3: v9.1 - YOLOv5 Forward Compatibility Updates

ultralytics/yolov3: v9.0 - YOLOv5 Forward Compatibility Release