Table 4. Listing and summarization of studies.

Study	Purpose	Target	Detection methods	Evaluation metrics	Challenges
Li et al. (2015)	To describe a novel approach for generating object proposals for ball detection	Sports balls	R-CNN, contouring, CHT,	mAP, Recall, Inference time	Small or distant objects, computational cost
Ali Shah et al. (2018)	To achieve an embedded approach for ball detection and tracking using image processing	Sports balls	Colour detection, Gaussian blur, contouring, centroid detection	Accuracy	Complex backgrounds, motion blur
Reno et al. (2018)	To present an innovative deep learning approach to tennis balls identification	Sports balls	CNN	Accuracy, Precision, Recall	Motion blur, high number of false positives
Burić, Pobar & Ivasic-Kos (2018a)	To compare YOLO and Mark R-CNN for handball detection in real-world conditions	Sports balls and humans	YOLOv2, Mask R-CNN	Precision, Recall, F1-score	Occlusion, computational cost, high number of false positives
Burić, Pobar & Ivašić-Kos (2018b)	To provide an overview of CNN detection methods for handball analysis	Sports balls and humans	YOLO, Mask R-CNN, MOG	Precision, Recall, F1-score	Occlusion, lighting, computational cost
Teimouri, Delavaran & Rezaei (2019)	To propose a low-cost ball detection method for football robots	Sports balls	CNN	Accuracy, Precision, Recall, Inference time	Lighting, motion blur, underfitting
Renolfi de Oliveira et al. (2019)	To investigate the performance of a vision system for object detection under constrained hardware, trained on a football dataset	Sports balls	SSD-MobileNet	Accuracy, Precision, mAP, Recall, F1-score, Inference time	Accuracy trade-off, computational cost
Barry et al. (2019)	To develop a lightweight real-time model (xYOLO)	Sports balls and goalposts	xYOLO	mAP, F1-score, Inference time	Accuracy trade-off
Deepa et al. (2019)	To describe a systemic approach for trajectory estimation of tennis balls	Sports balls	YOLO, SSD, Faster R-CNN	Accuracy, Inference time	Occlusion, motion blur
Calado et al. (2019a)	To propose a ball-detection system for boccia to motivate elderly physical activity	Sports balls	Contouring, centroid detection, colour detection	Accuracy	Lighting, underfitting
Calado et al. (2019b)	To have a versatile algorithm for boccia balls detection	Sports balls	Tiny-YOLO, HOG-SVM	Precision, AP, Recall, Inference time	Computational cost, high number of false negatives
Wang et al. (2019)	To present a high-speed stereo vision golf ball tracking system	Sports balls	ROI, P-tile, noise filtering	Accuracy, Recall, Inference time	Occlusion
Tian, Zhang & Zhang (2020)	To propose an anchor-free tennis balls object detector	Sports balls	YOLOv3	Accuracy, Precision, Recall, F1-score	Motion blur, small or distant objects
Zhang et al. (2020)	To propose an efficient solution for real-time golf ball detection and tracking	Sports balls	YOLOv3, YOLOv3-tiny, Faster R-CNN, Kalman filter	Precision, mAP, Inference time	Small or distant objects, underfitting
Sheng et al. (2020)	To propose a real-time one-stage algorithm based on feature fusion for table tennis balls detection	Sports balls	YOLOv3-tiny	mAP, Inference time	Underfitting
Fatekha, Dewantara & Oktavianto (2021)	To enhance detection algorithms for sports robotics in football using colour-based segmentation	Sports balls	Colour-based segmentation, morphological operations, contouring	Inference time	High number of false positives
Meneghetti et al. (2021)	To evaluate the performance of detection systems in constrained hardware	Sports balls	YOLO (v3, v4, tiny-v3 and tiny-v4), SSD (v2 and v3)	AP, Inference time	Computational cost
Hiemann et al. (2021)	To address the detection of small and fast-moving balls in sports in real-time	Sports balls	YOLOv3	Precision, AP, Recall, F1-score, IoU, Inference time	Underfitting, motion blur
Balaji, Karthikeyan & Manikandan (2021)	To research volleyball player detection using innovative Metaheuristic algorithms	Humans	Firefly, TLBO, Cuckoo Search	Accuracy, Precision, Recall	Occlusion
Pawar et al. (2021)	To create a robust tracking algorithm based on a custom rugby dataset to replicate industrial object detection	Sports balls	SSD-MobileNet	Accuracy, Inference Time	Underfitting, computational cost
Liu et al. (2021)	To propose a method that detect and matches players to their equipment with a single bounding box	Humans	YOLO, FPN	Accuracy, AP, IoU, Inference time	Occlusion, Overlap of bounding boxes
Hassan, Karungaru & Terada (2023)	To propose a method to detect handball events on football games	Sports balls and humans	YOLOv7, instance segmentation, Kalman filter, separating axis theorem	Accuracy	Occlusion, High number of false positives
Keča et al. (2023)	To explore the combination of HT and deep learning methods for ball detection on a low-powered device	Sports balls	Faster R-CNN	Precision, Recall, F1-score, IoU, Inference time	Computational cost
Li et al. (2023)	To improve immersiveness in live sports broadcasting via real-time 3D detection	Sports balls and humans	YOLOv3	Accuracy, Precision, Recall, IoU, Inference time	Complex backgrounds
Kulkarni et al. (2023)	To detect and identify table tennis strokes using ball trajectory data, eliminating the need for player-focused video and wearable sensors	Sports balls	YOLOv4, TrackNetv2	Accuracy, Precision, F1-score, Inference time	Accuracy
Zhao (2024)	To propose an improved version of YOLOv5 for football and player recognition	Sports balls and humans	YOLOv5	Precision, AP, mAP, Recall	Occlusion
Modi et al. (2024)	To propose a hybrid enhanced system for object tracking in videos	Sports balls	YOLO (v3, v5, v8), NMS. Gaussian blur	Precision, recall, F1-score, Inference time	Accuracy, occlusion, computational cost
Esfandiarpour, Mirshabani & Miandoab (2024)	To enhance detection algorithms for sports robotics in football	Sports balls	YOLOv8, colour detection, CHT, frame differencing	Accuracy, Inference time	High number of false positives, lighting
Li & Zhao (2024)	To propose an improved version of YOLOv5 to solve problems in tennis balls recognition	Sports balls	YOLO (v3, v4, v5), SSD, Faster R-CNN, FPN	mAP, Inference time	Computational cost
Decorte et al. (2024)	To introduce a predictive model for detecting padel hits based on audio signals, pose tracking, distance calculating framework, and a padel open dataset	Humans	YOLO, TrackNet, SED	Accuracy, F1-score	Occlusion
Fujimoto et al. (2024)	To enable table tennis ball detection with higher Accuracy using fine-tuning	Sports balls	Mask R-CNN, Faster R-CNN, RetinaNet	AP, Inference time	Computational cost
Luo, Quan & Liu (2024)	To improve the Accuracy of small fast-moving ball detection in sports	Sports balls	YOLOv8	Precision, mAP, Recall, F1-score	Small or distant objects
Li, Luo & Islam (2024)	To propose an hybrid YOLO-T2FLSTM detection system for basketball players and action recognition	Humans	YOLO-T2LSTM	Accuracy, IoU	Occlusion, Underfitting
Yang et al. (2024b)	To propose a novel model, YOLO-HGNet, to enhance feature learning, applied to badminton action recognition	Humans	YOLO-HGNet	Precision, mAP, Recall, F1-score	High number of false positives, computational cost
Hu et al. (2024)	To address the challenges of complex multi-object occlusion in basketball	Humans	YOLOv8	Accuracy	Occlusion
Fu, Chen & Song (2024)	To propose a YOLOv8n-based model capable of solving the challenges of large deformation and small size in football object detection	Sports balls and humans	HPDR-YOLOv8	Precision, mAP, Recall, Inference time	Computational cost
Solberg et al. (2024)	To automate the creation of player-specific football highlight videos	Sports balls and humans	YOLO	Accuracy, Inference time	Accuracy, Inference time
Yin et al. (2024)	To address the challenges posed by frequent occlusions and visual similarity of players in basketball	Humans	YOLOv8	Accuracy	Occlusion