Comparing shape descriptor methods for different color space and lighting conditions

Mukhopadhyay, Abhishek; Mukherjee, Imon; Biswas, Pradipta

doi:10.1017/s0890060419000398

Cited by 6 publications

(3 citation statements)

References 32 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of green light detection, the navigation algorithm uses a light intensity-based color segmentation algorithm. An extensive investigation by Mukhopadhyay et al (2019) explored diverse color space models under varying lighting conditions. Their study provided valuable insights, leading us to select the HSV color space model.…”

Section: Navigation Algorithmmentioning

confidence: 99%

Developing a Computer Vision Based System for Autonomous Taxiing of Aircraft

Gaikwad,

Mukhopadhyay,

Muraleedharan

et al. 2023

Aviation

Self Cite

View full text Add to dashboard Cite

Authors of this paper propose a computer vision based autonomous system for the taxiing of an aircraft in the real world. The system integrates both lane detection and collision detection and avoidance models. The lane detection component employs a segmentation model consisting of two parallel architectures. An airport dataset is proposed, and the collision detection model is evaluated with it to avoid collision with any ground vehicle. The lane detection model identifies the aircraft’s path and transmits control signals to the steer-control algorithm. The steer-control algorithm, in turn, utilizes a controller to guide the aircraft along the central line with 0.013 cm resolution. To determine the most effective controller, a comparative analysis is conducted, ultimately highlighting the Linear Quadratic Regulator (LQR) as the superior choice, boasting an average deviation of 0.26 cm from the central line. In parallel, the collision detection model is also compared with other state-of-the-art models on the same dataset and proved its superiority. A detailed study is conducted in different lighting conditions to prove the efficacy of the proposed system. It is observed that lane detection and collision avoidance modules achieve true positive rates of 92.59% and 85.19%, respectively.

show abstract

Section: Navigation Algorithmmentioning

confidence: 99%

Developing a Computer Vision Based System for Autonomous Taxiing of Aircraft

Gaikwad,

Mukhopadhyay,

Muraleedharan

et al. 2023

Aviation

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the electronic component detection problem in the PCB assembly scene, the current research mainly includes six aspects: small size object detection [37,38], PCB positioning [39,40], electronic component detection [41][42][43][44][45][46], model lightweight [47,48] and real-time detection [49,50]. Li et al proposed a detection method for small-sized PCB electronic components based on multiple detection heads.…”

Section: Electronic Component Detection In Pcb Assembly Scenementioning

confidence: 99%

“…Mukhopadhyay et al used a color space conversion model to convert RGB to YCbCr when detecting IC (integrated circuits) on a circuit board. Then, three shape descriptors were combined to achieve the best detection effect [44]. To improve the accuracy of object detection of electronic components on PCB, Liu et al proposed a new box regression loss function based on YOLOv4, called Gaussian Intersection Joint (GsIoU), which uses the Gaussian function to predict boxes under different anchors.…”

Section: Electronic Component Detection In Pcb Assembly Scenementioning

confidence: 99%

Balanced-YOLOv3: Addressing the Imbalance Problem of Object Detection in PCB Assembly Scene

Chen

et al. 2022

Electronics

View full text Add to dashboard Cite

The object detection algorithm of the PCB (Printed Circuit Board) assembly scene based on CNN (Convolutional Neural Network) can significantly improve the production capacity of intelligent manufacturing of electronic products. However, the object class imbalance in the PCB assembly scene, the multi-scale feature imbalance, and the positive/negative sample imbalance in the CNN have become critical problems restricting object detection performance. Based on YOLOv3, this paper proposes a class-balanced Train/Val (Training set/Validation set) split method for object class imbalance, an additional feature fusion strategy for multi-scale feature imbalance, and an efficient anchor concept for positive/negative sample imbalance. These three contributions are Balanced-YOLOv3. After experimental verification, compared with other YOLOv3 series algorithms, the mAP@.5 (Mean Average Precision at Intersection over Union threshold 0.5) and mAP@.5:.95 (average mAP over different Intersection over Union thresholds, from 0.5 to 0.95, step 0.05) of Balanced-YOLOv3 have achieved the best results and ranked third in the metrics of parameter and inference time. Compared with other current anchor-based object detection algorithms, Balanced-YOLOv3 has excellent detection performance and low computational complexity, which effectively solves the problem of imbalanced object detection in PCB assembly scenarios.

show abstract