High-Profile VRU Detection on Resource-Constrained Hardware Using YOLOv3/v4 on BDD100K

Castello, Vicent Ortiz; Igual, Ismael Salvador; Catala, Omar Del Tejo; Pérez-Cortés, Juan-Carlos

doi:10.3390/jimaging6120142

Cited by 12 publications

(5 citation statements)

References 29 publications

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“…This technique enables the model to learn how to recognize objects at a smaller scale than usual and considerably reduces the demand for large batch sizes during training. This approach not only enhances the model's ability to detect small targets and improve its generalization ability but also enriches the feature information in the images (Ortiz et al., 2020). In this study, chicken part images were collected and a dataset was established.…”

Section: Resultsmentioning

confidence: 99%

“…In contrast, YOLOV3 did not perform as well as these models. Unlike the FPN layer used in YOLOV3, YOLOV4 incorporates a This approach not only enhances the model's ability to detect small targets and improve its generalization ability but also enriches the feature information in the images (Ortiz et al, 2020). In this study, chicken part images were collected and a dataset was established.…”

Section: Comparison Of the Performance Of Different Detection Methodsmentioning

confidence: 99%

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Research on automatic classification and detection of chicken parts based on deep learning algorithm

Chen,

Peng,

Cai

et al. 2023

Journal of Food Science

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Accurate classification and identification of chicken parts are critical to improve the productivity and processing speed in poultry processing plants. However, the overlapping of chicken parts has an impact on the effectiveness of the identification process. To solve this issue, this study proposed a real‐time classification and detection method for chicken parts, utilizing YOLOV4 deep learning. The method can identify segmented chicken parts on the assembly line in real time and accurately, thus improving the efficiency of poultry processing. First, 600 images containing multiple chicken part samples were collected to build a chicken part dataset after using the image broadening technique, and then the dataset was divided according to the 6:2:2 division principle, with 1200 images as the training set, 400 images as the test set, and 400 images as the validation set. Second, we utilized the single‐stage target detector YOLO to predict and calculate the chicken part images, obtaining the categories and positions of the chicken leg, chicken wing, and chicken breast in the image. This allowed us to achieve real‐time classification and detection of chicken parts. This approach enabled real‐time and efficient classification and detection of chicken parts. Finally, the mean average precision (mAP) and the processing time per image were utilized as key metrics to evaluate the effectiveness of the model. In addition, four other target detection algorithms were introduced for comparison with YOLOV4‐CSPDarknet53 in this study, which include YOLOV3‐Darknet53, YOLOV3‐MobileNetv3, SSD‐MobileNetv3, and SSD‐VGG16. A comprehensive comparison test was conducted to assess the classification and detection performance of these models for chicken parts. Finally, for the chicken part dataset, the mAP of the YOLOV4‐CSPDarknet53 model was 98.86% on a single image with an inference speed of 22.2 ms, which was higher than the other four models of YOLOV3‐Darknet53, YOLOV3‐MobileNetv3, SSD‐MobileNetv3, and SSD‐VGG16 mAP by 3.27%, 3.78%, 6.91%, and 6.13%, respectively. The average detection time was reduced by 13, 1.9, 6.2, and 20.3 ms, respectively. In summary, the chicken part classification and detection method proposed in this study offers numerous benefits, including the ability to detect multiple chicken parts simultaneously, as well as delivering high levels of accuracy and speed. Furthermore, this approach effectively addresses the issue of accurately identifying individual chicken parts in the presence of occlusion, thereby reducing waste on the assembly line.Practical ApplicationThe aim of this study is to offer visual technical assistance in minimizing wastage and resource depletion during the sorting and cutting of chicken parts in poultry production and processing facilities. Furthermore, considering the diverse demands and preferences regarding chicken parts, this research can facilitate product processing that caters specifically to consumer preferences.

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Section: Resultsmentioning

confidence: 99%

Section: Comparison Of the Performance Of Different Detection Methodsmentioning

confidence: 99%

Research on automatic classification and detection of chicken parts based on deep learning algorithm

Chen,

Peng,

Cai

et al. 2023

Journal of Food Science

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“…Table 3 gives the AP50 and mAP of the proposed method and six other classical objection detection algorithms. For AD-Faster-RCNN [25], SSD [16], YOLOv4-416 [28], and YOLOv4, as there are no detection data for traffic lights, only their pedestrian and vehicle detection results are provided, and the number of their detection categories N = 2. For MS-DAYOLO [26], YOLOv5, YOLOv6, YOLOv7-tiny [27], and the method in this paper, N = 3.…”

Section: Detection Resultsmentioning

confidence: 99%

“…Shuiye Wu proposed a YOLOX based network model for multi-scale object detection tasks in complex scenes [27]. Vicent ortiz castell ó In order to help reduce accidents in the advanced driver assistance system, the original Leaky ReLU convolution activation function in the original YOLO implementation is replaced by the cutting-edge activation function in the YOLOv4 network to improve the detection performance [28]. When identifying obstacles in front of the blind, the obstacle detection algorithms mentioned above often have missed detection and false detection due to the diverse types of obstacles and complex conditions, such as occlusion, low contrast between target and background, and small target size.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Deep Learning Model for Obstacle and Traffic Light Detection Based on YOLOv5

Zhang

Yang

2023

Electronics

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Timely detection of dynamic and static obstacles and accurate identification of signal lights using image processing techniques is one of the key technologies for guidance robots and is a necessity to assist blind people with safe travel. Due to the complexity of real-time road conditions, current obstacle and traffic light detection methods generally suffer from missed detection and false detection. In this paper, an improved deep learning model based on YOLOv5 is proposed to address the above problems and to achieve more accurate and faster recognition of different obstacles and traffic lights that the blind may encounter. In this model, a coordinate attention layer is added to the backbone network of YOLOv5 to improve its ability to extract effective features. Then, the feature pyramid network in YOLOv5 is replaced with a weighted bidirectional feature pyramid structure to fuse the extracted feature maps of different sizes and obtain more feature information. Finally, a SIoU loss function is introduced to increase the angle calculation of the frames. The proposed model’s detection performance for pedestrians, vehicles, and traffic lights under different conditions is tested and evaluated using the BDD100K dataset. The results show that the improved model can achieve higher mean average precision and better detection ability, especially for small targets.

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“…From text analysis [1], [2] or pedestrian detection [3], [4] to healthcare [5], [6], it is unquestionable that Artificial Intelligence (AI) has become more and more useful in almost…”

Section: Introductionmentioning

confidence: 99%

Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks

et al. 2022

Self Cite

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The a priori probability of a dataset is usually used as a baseline for comparing a particular algorithm's accuracy in a given binary classification task. ZeroR is the simplest algorithm for this, predicting the majority class for all examples. However, this is an extremely simple approach that has no predictive power and does not describe other dataset features that could lead to a more demanding baseline. In this paper, we present the Extended A Priori Probability (EAPP), a novel semi-supervised baseline metric for binary classification tasks that considers not only the a priori probability but also some possible bias present in the dataset as well as other features that could provide a relatively trivial separability of the target classes. The approach is based on the area under the ROC curve (AUC ROC), known to be quite insensitive to class imbalance. The procedure involves multiobjective feature extraction and a clustering stage in the input space with autoencoders and a subsequent combinatory weighted assignation from clusters to classes depending on the distance to nearest clusters for each class. Class labels are then assigned to establish the combination that maximizes AUC ROC for each number of clusters considered. To avoid overfit in the combined feature extraction and clustering method, a cross-validation scheme is performed in each case. EAPP is defined for different numbers of clusters, starting from the inverse of the minority class proportion, which is useful for a fair comparison among diversely imbalanced datasets. A high EAPP usually relates to an easy binary classification task, but it also may be due to a significant coarse-grained bias in the dataset, when the task is previously known to be difficult. This metric represents a baseline beyond the a priori probability to assess the actual capabilities of binary classification models.

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High-Profile VRU Detection on Resource-Constrained Hardware Using YOLOv3/v4 on BDD100K

Cited by 12 publications

References 29 publications

Research on automatic classification and detection of chicken parts based on deep learning algorithm

Research on automatic classification and detection of chicken parts based on deep learning algorithm

An Enhanced Deep Learning Model for Obstacle and Traffic Light Detection Based on YOLOv5

Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks

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